Toward Sustainable Electricity Markets: Merit-Order Dynamics on Photovoltaic Energy Price Duck Curve and Emissions Displacement

Abstract

This paper examines how the slope of the merit-order curve and the share of non-zero-dollar dispatched energy affect photovoltaic (PV) price cannibalization and the declining market value of all generation types. Using historical merit-order data from Alberta, Canada—during its coal-to-gas transition—we simulated the introduction of zero-marginal-cost PV offers. The increased PV penetration rapidly suppresses midday electricity prices, forming a “duck curve” that challenges solar project economics. Emission reductions improve with rising carbon prices, indicating environmental benefits despite declining market revenues. Years with steeper merit-order slopes and lower non-zero-dollar dispatch shares show intensified price cannibalization and a reduced PV market value. The integration of battery storage alongside PV significantly flattened daily price profiles—raising the trough prices during charging and lowering the highest prices during discharging. While this reduces price volatility, it also diminishes the market value of all generation types, as batteries discharge at zero marginal cost during high-price hours. Battery arbitrage remains limited in low- and moderate-price regimes but becomes more profitable under high-price regimes. Overall, these dynamics underscore the challenges of integrating large-scale PV in energy-only markets, where price cannibalization erodes long-term investment signals for clean energy technologies. These insights inform sustainable energy policy design aimed at supporting decarbonization, and investment viability in liberalized electricity markets.

1. Introduction

1.1. Background

Deregulated wholesale electricity markets operate based on economic dispatch, in which the generation is dispatched from the lowest to highest offer price in a supply curve until the demand is met. The supply curve in the electric market is called the ‘merit-order curve’ [1], with the final dispatched generation in the merit order setting the market price for that hour. Variable-output renewable energy (VRE), such as solar and wind, has no fuel costs and is commonly offered at a price of zero dollars, thus displacing the highest variable cost generation, reducing the market price when they are generating. As a result, wind and solar energy tend to suppress wholesale prices, and their increased presence is known as the “merit-order effect” of renewable energy [2,3,4,5,6,7,8,9,10,11,12]. In the case of solar specifically, the shape of the net load (the electricity demand that remains after VRE has been dispatched [13]) begins to resemble a duck with a profound minimum during midday hours as the levels of photovoltaic (PV) energy increase, and is commonly referred to as the “Duck Curve” or “Duck Chart” [14,15,16,17]. The duck curve phenomenon was identified in 2008 by the National Renewable Energy Laboratory (NREL) [14]. In 2013, the California Independent System Operator (CAISO) coined the name Duck Chart [16]. The phenomenon of duck curves has been well-documented in jurisdictions with large utility-scale and rooftop PV penetration, such as California [18], Germany [19] and Australia [17], which have all experienced a resulting ‘price cannibalization’ as PV levels have increased. In California, the shape of the duck curve has changed, resembling a canyon with a high depth and steep walls [20].

Blazquez et al. studied the relationship between liberalized market structures and policies that promote VRE [21]. The authors find the “renewable energy policy paradox” that hinders 100% of VRE penetration regardless of the market location and type of VRE. On one side, the market-clearing price is established by marginal costs; on the other side, VRE has marginal costs near zero. Then, the paradox refers to the presence of a limit of VRE penetration after which VRE developers may not recover their investment, as the market prices would be near zero. The price cannibalization caused by VRE also affects the revenues of conventional power plants, such as coal and gas. In addition to lowering its market price, solar PV can also suppress prices for other market participants as the solar output tends to coincide with high-demand periods when prices tend to be higher than average for the entire market [22]. To compensate for these revenue losses, dispatchable power plants may increase their price offers when the VRE output is lower, increasing overall market price volatility [5]. It has been reported that PV penetration causes less volatility than wind because its output pattern is more predictable across the day and the year [23,24]. Previous research has found that price cannibalization may stabilize in the medium to longer term as the market adapts to VRE penetration [25], as unprofitable power plants (often less flexible) are decommissioned and replaced with new dispatchable resources [5]. However, if VRE levels increase to a point where the market is unable to adapt to ensure adequate revenues to warrant resource adequacy, market structures themselves may need to be adapted [26].

The rate at which the market prices drop in any given hour due to increased PV generation depends on the slope (offer price/offer volume) of the merit-order curve: the steeper the slope, the higher the price drop [19]. Previous research has explored the economic outcomes of the merit-order effect, applying empirical analysis to past market behavior [3,6,8,10,27,28], theoretical modeling [2,5], and counterfactual price scenarios [29]. The market value refers to the generation-weighted mean electric price that a power plant gets paid for its generation, only considering the wholesale market price [25,30]. This study examines the historical behavior of the merit order in Alberta, Canada, to examine how the slope and the zero-dollar dispatch portion affect the price cannibalization with increasing levels of modeled solar PV energy.

Antweiler and Muesgens analyzed the short- and long-term merit order effects of a theoretical system composed of base- and peak-load assets [5]. Their results showed that high-price spikes are reduced during high demand when VRE was present in the short term. This would produce losses to “old world” assets that used to generate revenue from price spikes to recover fixed costs. In the long term, the study found that the merit-order effect would vanish when the peak-load assets induce high-magnitude price spikes to recover their fixed costs and compensate for the VRE-induced reduction in price spikes. In the long term, the merit-order effect evolves to zero, in which generators adapt their price–quantity offers to cover their lifetime costs. The mathematical model of Antweiler and Muesgens [5] is based on the peak-load pricing model developed by Steiner [31] and Boiteux [32]. The peak-load pricing model is applied to goods and services with time-varying demand, low or null storage ability, and low short-run ramp-up capacity, in which fixed costs are considerably higher than their variable costs. The model maximizes social welfare by finding the optimal mix capacity and its related price.

The electricity price volatility (frequency and magnitude of price spikes) decreases with PV share expansion, increasing as the wind share rises [23]. Kyritsis et al. found that the mean and the standard deviation of the price diminish as the PV generation share increases from 0 to 21% in the German electricity market from 2010 to 2015, from 43.31 to 28.03 US dollars per megawatt-hour (USD/MWh) and from 12.13 to 9.68 USD/MWh, respectively [23]. The kurtosis of the price distribution fell from 5.837 to 0.955, indicating a reduction in major price fluctuations. However, when the wind share rises from 0 to 55%, the standard deviation and kurtosis increase from 10.22 to 14.34 USD/MWh and from 0.35 to 9.03, respectively. Figueiredo and Silva showed that wind variability translates into a merit-order effect mirrored by market price volatility. PV generation does not cause that volatility due to its smoother output variations [8]. The research was performed in the joint Spanish and Portuguese market from 2008 to 2017.

Ederer used a counterfactual time-series model to calculate the market value of offshore wind power plants using data on demand and supply bid curves from 2006 to 2014 in the German market [25]. The demand curve was kept static for every hour, while the bid curve was modified to accommodate feed-in wind curves. The long-term scenario was devised by substituting the baseload with wind generation to simulate the adaptation of the electric system. The baseload was composed of nuclear power and carbon-intensive generation. The results showed that the market value of offshore and onshore wind power plants was related to the amount of energy they provide, i.e., geographical locations that supply higher energy received a lower market value. The steadiness of offshore wind production caused fewer price spikes (negative and positive); thus, a steadier wind output produced less electricity price volatility [32]. The rapid expansion of wind electricity in the short term diminished the market price, while, in the long term, the decline in prices vanished. This behavior was related to the oversupply of generation occurring in the wind’s short-term entrance.

Ciarreta et al. assessed the economic benefits of public support schemes in the Spanish market [29]. The model computed counterfactual scenarios of the wholesale market price without the existing VRE in the system from 2008 to 2012. Then, the difference between the actual and counterfactual system costs is compared to the VRE subsidies to promote their development. The results showed that, in 2009 and 2010, the merit-order effect of VRE reduced the wholesale market price, bringing an economic benefit that surpassed the incentives by 231% and 137%, respectively. On the contrary, from 2010 to 2012, the public support paid to VRE increased the market costs; i.e., if VRE had not been in the system, the electricity net costs would have been 5%, 45%, and 39% less than the actual costs, respectively.

Restel and Say examined the impact of electricity tariff structures on the behavior of households with photovoltaic and battery energy storage systems (PV-BESS) in Melbourne, Australia [33]. Using an optimization model, they found that time-varying export tariffs—i.e., pricing for discharged energy—significantly reshaped electricity usage and grid interaction, effectively flattening the duck curve. The study concludes that time-of-export tariffs offer a cost-effective policy tool to enhance grid stability and facilitate the integration of distributed solar generation.

The comparison of the merit-order effect is limited due to the diversified market designs and heterogeneous mathematical models. However, behavioral trends can be escalated to different markets that meet similar conditions or are behind in their transition toward renewables. An interested reader can find a summary of merit-effect studies in Bublitz et al. [34] and Figueiredo and Silva [8].

Alberta’s electricity system, historically heavily reliant on fossil fuels, is unique in Canada for its competitive energy-only market, where generators compete in a wholesale electricity market [35]. The wholesale electricity market operates under a single hourly price across the province and is primarily supplied by domestic generation, with imports accounting for only 5.4% of the market share in 2022. Market participants may submit up to seven hourly price–quantity offers and are required to offer their full available capacity each hour [35]. Offers must range between 0 to 999.99 Canadian dollars per megawatt-hour (CAD/MWh). Wind and solar generation (as well as available imports) are required to offer all of their energy at 0 CAD/MWh, while a significant portion of thermal units’ operating blocks are commonly offered at a zero-dollar price to ensure minimum stable generation levels are met, to dispatch inflexible generation. Alberta has a high level of industrial cogeneration (cogen) facilities, often supplying around 30% of the wholesale market with 0 CAD/MWh offers. In contrast, flexible generation can be offered at prices that recover operating and fixed costs while allowing for profit margins [36]. Non-zero-priced offers tend to start close to marginal fuel costs, then slope upwards to more strategic offers closer to the final price.

Alberta has a legislated target to supply 30% of its annual electricity production with renewable electricity by 2030 [37], and, while renewable energy is growing in recent years, thermal electricity generation accounted for over 80% of the electricity generation in Alberta during the study period. Coal historically represented the largest generation share in Alberta but fell from over 62% of the market in 2010 to 13% in 2022 and was retired completely in July 2023 [38]. Figure 1 presents the generation share from 2010 to 2021. Generation from natural gas, including simple cycle (SC), combined cycle (CC), and cogen output, grew over the past decade, while several coal units were converted to gas-fired steam, such that natural gas overtook coal as the largest fuel source for electricity in 2018. During the study period, wind had the largest VRE annual share, ranging from 5 to 8% of the annual generation. Wind and solar generation have expanded in recent years due to declining costs and the implementation of an industrial carbon pricing system, which is set to rise to 170 Canadian dollars per tonne of carbon dioxide equivalent (CAD/t_CO2e) by 2030 [39]. The first commercial solar PV plant was commissioned in 2017 and is projected to increase to 3800 MW of installed capacity by 2026.

On average, the PV production potential in Alberta is 1.28 MWh/kW-yr, which is higher than the Canadian average of 1.13 MWh/kW-yr [40]. The relatively dry climate and low temperatures benefit electricity production compared to regions with warmer weather [41]. Furthermore, renewable electricity can generate carbon emissions credits as part of the provincial greenhouse gas policy framework. Nevertheless, solar’s growth will be affected by its ability to capture viable market prices into the future.

On the other hand, solar energy’s impact on the market price is affected by the relative proportion of non-zero-dollar merit offers to the hourly demand and the slope of the merit order. Empirically, we found that, the smaller the ratio of non-zero-dollar offers to the demand, the steeper the offer slope, and this has historically corresponded to higher hourly prices. We defined the slope of the non-zero-dollar dispatch portion as the division of the hourly price and the non-zero-dispatched energy, which is different than the study by Hirth [19], who refers to the slope of the entire dispatched generation in the merit order. Steep slopes vary from 0.05 to 0.08 (CAD/MWh)/MW, medium slopes vary from 0.03 to 0.04 (CAD/MWh)/MW, and low slopes vary from 0.008 to 0.014 (CAD/MWh)/MW at the times of highest values. The average merit order profiles over the study period were classified into three distinct categories [42]: (1) a steep slope with the smallest non-zero-dollar offer ratio, (2) a medium slope with a medium ratio, and (3) a low-flat slope with a large ratio. Figure 2 presents (a) the annual mean hourly historical marginal price, (b) the annual mean hourly slope, and (c) the annual mean hourly non-zero-dollar offer portion. The steep slope merit-order type is colored in green, and medium and low-flat slopes are colored in red and blue, respectively.

1.2. Aim of the Study

This study analyzes how PV integration affects market prices, generator market value, and the system’s emissions, offering insights into designing sustainable electricity systems that balance environmental, economic, and social needs. By introducing incremental levels of simulated solar output into historic merit orders, this paper examines the following:

• How do historical pricing regimes in Alberta’s electricity market influence the extent of PV price cannibalization?
• What is the impact of increasing PV penetration on the market value of different generation technologies?
• Which generation is displaced by PV energy under different historical carbon pricing scenarios?
• How does battery storage influence price stabilization and market value in a PV-integrated electricity system?

1.3. Contribution of Work

The contribution of this study lies in its simulation of the merit-order effect within electricity markets characterized by varying merit-order slopes, to demonstrate how price cannibalization is influenced by both the steepness of the merit-order curve and the proportion of zero-marginal-cost generation. While previous theoretical research has established that the merit-order effect is sensitive to changes in the slope of the merit curve [42,43], this study advances the literature by quantifying the merit-order effect in historical data and the reduction in market value across different generation technologies. In doing so, it addresses the renewable energy policy paradox—namely, the tension between promoting renewable deployment and preserving the economic viability of such investments.

We employ counterfactual simulations based on historical time-series data to assess the extent to which accelerated solar PV deployment suppresses average market prices and reduces the market value of PV. The methodological framework is adapted from Durán-Castillo et al. [42] and aligns closely with that of Ciarreta et al. [29] and Ederer [25], all of which utilize the observed offer behavior from market participants, rather than purely theoretical constructs. A key advantage of this approach is its empirical foundation in actual bidding data; however, it does not account for endogenous adjustments in market participant behavior in response to the introduction of zero-marginal-cost generation. To partially address this limitation, we complement the quantitative analysis with a qualitative discussion of the observed historical changes in bidding strategies and offer behavior under evolving market conditions.

Furthermore, we assess the broader system-level implications of PV integration by constructing load and emissions “duck curves” under varying levels of solar deployment. The analyzed period encompasses a dynamic transition in the generation fleet, including episodes of both market tightness and overcapacity, shifts in the marginal generation mix due to the commissioning of new natural gas-fired plants, and the implementation of major industrial carbon pricing reforms.

The modeling framework incorporates real-world dispatch behavior derived from Alberta’s historical merit-order evolution, capturing the dynamic transition from coal to natural gas generation. The methodological synthesis presented herein provides a lens to examine how transitions in legacy generation infrastructure reshape marginal price formation and, consequently, affect the integration of VRE sources such as PV.

2. Methodology

This section explains the procedure used to simulate the merit-order effect and generate the price and load duck curves.

2.1. Simulation of Merit-Order Effect and Cannibalized Price

This research sought to understand how the merit-order curve and the non-zero-dollar dispatch portion of the merit order influence the market value of PV energy. To achieve this, we simulated the integration of hypothetical PV plants and analyzed their impact on market dynamics.

The historical merit-order data were modified under the assumption that all hypothetical PV energy enters the market at zero marginal cost (0 CAD/MWh). This displaced the most expensive offers in each hour, subsequently lowering market prices. Eight scenarios were modeled, representing PV fleet capacities of 0.1, 0.4, 0.8, 1, 1.5, 2, 2.5, and 3 GW.

A conservative assumption was applied: the historical price–quantity offers were maintained without accounting for strategic bidding reactions to PV penetration. This assumption highlights the potential magnitude of price cannibalization, as strategic bids are displaced, and prices settle based on less flexible units offering closer to their marginal fuel costs. This approach also revealed which technology and fuel types are most vulnerable to displacement by solar PV generation.

The simulation process involved the following steps:

• Simulate hourly PV energy profiles: Generate hypothetical PV energy profiles for each hour of the historical years;
• Input PV generation: Introduce the PV generation into the historical hourly merit order as zero-dollar offers;
• Accumulate generation offers: Sum the generation offers in the merit order until the total supply meets the hourly demand;
• Determine the counterfactual price: Identify the price of the last dispatched unit, referred to as the “impacted” price;
• Analyze displacement: Quantify the remaining and displaced dispatch and emissions after introducing hypothetical PV energy into the merit order.

The historical demand was met by dispatching supply to meet demand as seen in Equation (1):

$${\mathrm{d}}_{\mathrm{j}}=\sum _1^{\mathrm{n}}{\mathrm{D}}_{\mathrm{ij}}$$

(1)

where:

${\mathrm{d}}_{\mathrm{j}}$ = demand at hour j (in MW);

${\mathrm{D}}_{\mathrm{ij}}$ = dispatch of plant i at hour j (in MW);

n = number of hour of the year, it spans until 8760 and 8784 for non-leap and leap years.

The demand minus hypothetical PV power (${{\mathrm{d}}^{\prime }}_{\mathrm{j}})$ is the sum of the remaining dispatch after the introduction of PV, as shown in Equations (2) and (3):

$${{\mathrm{d}}^{\prime }}_{\mathrm{j}}={\mathrm{d}}_{\mathrm{j}}-{\mathrm{E}}_{\mathrm{j}}$$

(2)

$${{\mathrm{d}}^{\prime }}_{\mathrm{j}}=\sum _1^{\mathrm{n}}{{\mathrm{D}}^{\prime }}_{\mathrm{ij}}$$

(3)

where:

${{\mathrm{d}}^{\prime }}_{\mathrm{j}}$ = demand (MW) after PV hypothetical output at hour j;

${\mathrm{E}}_{\mathrm{j}}$ = PV hypothetical output (MW) at hour j;

${{\mathrm{D}}^{\prime }}_{\mathrm{ij}}$ = dispatch of plant i (MW) at hour j after the introduction of PV output at hour j.

The historical dispatch of electricity from conventional generators declines with the hypothetical introduction of photovoltaic (PV) energy. This effect is illustrated in Figure 3a,b, which depict the merit order for a representative hour with a system demand of 9.58 GW. Figure 3a shows the actual historical merit-order dispatch, while Figure 3b incorporates simulated PV generation, represented in green. In this scenario, PV energy from a 3-GW PV plant operating at a 58% capacity factor is introduced, contributing zero-dollar-price energy to the system. The inclusion of this PV generation reduces the marginal clearing price from 46.06 CAD/MWh (Figure 3a) to 26.96 CAD/MWh (Figure 3b). This price reduction results from the displacement of higher-cost generation units (located on the right-hand side of the merit-order curve) by the zero-dollar PV energy price. In this study, we refer to the market price following the integration of PV energy as the impacted price.

It is important to note that, in both Figure 3a,b, generation resources with a bid price of 0 CAD/MWh appear on the left side of the merit order and are plotted below the horizontal axis purely for visualization purposes.

We computed the impacted price for each hour of the year under each of the eight modeled PV capacities.

Subsequently, after computing the impacted prices, we calculated the market value of each plant type under each scenario. The market value is the weighted payment received for the produced generation, as shown in Equation (4), taken from Hirth [30]:

$${\mathrm{C}}_{\mathrm{i}}={\displaystyle \frac{\sum _1^{\mathrm{j}}{\mathrm{P}}_{\mathrm{j}}{\mathrm{G}}_{\mathrm{i}\mathrm{j}}}{\sum _1^{\mathrm{j}}{\mathrm{G}}_{\mathrm{i}\mathrm{j}}}}$$

(4)

where ${\mathrm{C}}_{\mathrm{i}}$ is the market value of a plant or group of plants i, ${\mathrm{P}}_{\mathrm{j}}$ is the price at hour j, and ${\mathrm{G}}_{\mathrm{ij}}$ is the generation of a plant or group of plants i at hour j. The value factor is found by dividing the market value of a plant or group of plants i by the mean market price and indicates the relative price of a particular technology compared to the overall fleet, from Hirth [30].

Batteries on Price Stabilization and Market Value

To evaluate the influence of battery energy storage systems (BESSs) on market price stabilization following the large-scale deployment of PV capacity, a simulation was conducted incorporating 500 MW/1000 MWh of battery storage into a system with 3000 MW of PV. In the model, the BESS units were assumed to discharge at a marginal cost of 0 CAD/MWh while purchasing energy at the prevailing market price during charging periods. Charging was scheduled to occur during hours with the lowest market prices, thereby increasing those prices by necessitating the dispatch of higher-cost generation. Conversely, discharging was scheduled during periods of peak market prices, reducing prices by displacing high-priced generation offers with zero-priced battery energy. The total storage capacity was modeled as comprising ten utility-scale lithium-ion BESS units—hereafter referred to as ‘batteries’—each with a power capacity of 50 MW and an energy capacity of 100 MWh, enabling continuous discharge at 50 MW for a duration of two hours [44]. The battery systems were modeled with a round-trip efficiency of 86%, meaning that 86% of the electrical energy input during charging is recovered during the discharge phase [45].

2.2. Modeling and Validating PV System Performance

We computed eight scenarios for PV installed capacities (sizes) of 0.1, 0.4, 0.8, 1, 1.5, 2, 2.5, and 3 GW with equal distribution across potential development locations in the province. The objective was to introduce PV energy patterns into multiple years of historical data. The PV production profiles were computed for fixed racks with an azimuth of 180° (due south) and the tilt angle that produced the highest simulated generation at each site. During the computed years, the median annual capacity factor varied from 14% to 17%. The energy outputs were estimated from grid-connected PV systems employing System Advisory Model (SAM) Version 2020.2.29, a techno-economic software model developed by the National Renewable Energy Laboratory (NREL) [46]. We considered a PV module of Jinko Solar Co. Ltd. (Shanghai, China) JKM355M-72-V, with inverter of Power Electronics FS2000CU [400 V], DC to AC ratio of 1.08, and ground coverage ratio of 0.33 similar to those of the first solar power plant commissioned in Alberta.

Firstly, we calculated the hourly direct normal irradiance (DNI) using the PVLIB MATLAB toolbox developed by Sandia National Laboratories. PVLIB employs the modified direct insolation simulation code (DISC) model developed by Perez et al. (1992) [47] from statistical analysis of measured global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI) [48]. Then, we used SAM to calculate DHI, employing the Perez 1990 model described by Perez et al. (1988) [49] and Perez et al. (1990) [50]. For the calculation of the direct current (DC) power output, SAM employs the California Energy Commission (CEC) Module Model, a single-diode equivalent circuit model described in De Soto et al. (2006) [51]. The inter-row shading loss algorithm is described by Deline et al. (2013) [52], and Appelbaum and Bany (1979) [53]. A description of the nominal operating cell temperature (NOCT) method is available in De Soto (2004) [54]. Finally, the PV system’s hourly alternating current (AC) output is obtained in SAM employing the Sandia model, an empirical DC-to-AC power conversion inverter model described in King (2017) [55].

The AC output is calculated for every hour of the year; the model calculates the conversion efficiencies of the module and the inverter. The model assumes that the operation is performed at the maximum power point voltage [46]. The maximum power point voltage is the voltage at which the maximum power is produced [56]. The inverter model calculates the conversion efficiencies of solar energy to DC electricity and DC to AC electricity, accounting for inverter saturation and clipping. The description of the entirely SAM procedure is available in Gilman (2015) [46].

We validated the modeled PV output by comparing output to actual reported generation of the existing Brooks Solar plant which has an AC capacity of 15 MW. We calculated the relative bias error (rMBE) and the root mean square error (RMSE) defined in Equations (5) and (6) as per the work of Brown and O’Sullivan [57], where x_mod indicates the modeled value and x_mea indicates the measured value of PV output:

$$\mathrm{r}\mathrm{M}\mathrm{B}\mathrm{E}={\displaystyle \frac{\sum \left({\mathrm{x}}_{\mathrm{m}\mathrm{o}\mathrm{d}}-{\mathrm{x}}_{\mathrm{m}\mathrm{e}\mathrm{a}}\right)}{\sum {\mathrm{x}}_{\mathrm{m}\mathrm{e}\mathrm{a}}}}$$

(5)

$$\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}=\sqrt{{\displaystyle \frac{\sum {\left({\mathrm{x}}_{\mathrm{m}\mathrm{o}\mathrm{d}}-{\mathrm{x}}_{\mathrm{m}\mathrm{e}\mathrm{a}}\right)}^2}{\sum {{\mathrm{x}}_{\mathrm{m}\mathrm{e}\mathrm{a}}}^2}}}$$

(6)

Approximately 84% of the annual solar energy in Alberta is generated during the eight-month period from March to October. During this timeframe, the rMBE and rMSE are 0.016 and 0.17, respectively. Higher errors occurred between modeled and measured PV outputs in the winter that were attributable to snow covering that is not included in the model. While the difference between modeled and reported data in the winter months has little effect on the overall findings due to the relatively small amounts of energy generated during these months, the results can be interpreted as if regular snow removal was occurring at all the hypothetical solar sites.

2.3. Data

2.3.1. Merit Order

Alberta’s electricity market exhibits year-to-year variability in historical pricing dynamics [42]. Through detailed analysis, we observed that both the shape of the merit-order curve and the non-zero-dollar portion of the merit order fluctuated annually. To capture these dynamics, we utilized a dataset spanning from January 2010 to October 2022, which included energy price–quantity offers submitted by generators for each hour of the year.

This dataset consists of pairs representing the available generation capacity to be dispatched (measured in megawatt-hours, MWh) and the corresponding price per megawatt-hour (CAD/MWh). On average, about 200 offers are submitted each hour, with the quantity of energy available varying significantly from hour to hour. This variability highlights the dynamic nature of Alberta’s electricity market and provides a robust foundation for analyzing the impacts of integrating PV energy on market behavior.

2.3.2. Emissions

The merit-order dataset includes plant-specific emission rates (t_CO2e/MWh). For each simulated scenario, total system emissions and the emissions displaced by PV generation were calculated.

2.3.3. Generation Dispatch

As previously described, the merit-order dataset comprises historical hourly energy-price offers for each power plant. This dataset was utilized to compute the load after PV energy, subtracting the hourly PV generation from the hourly system demand. Subsequently, the displaced generation by plant type was determined by integrating PV energy into the merit order and analyzing the resulting changes in dispatch.

2.3.4. Meteorological Data

The modeled PV sites were selected based on their photovoltaic potential—ranging from approximately 1100 kWh/kWp in northern Alberta to 1400 kWh/kWp in the south [58]—as well as their proximity to transmission infrastructure, population centers, and industrial loads, making them strong candidates for future development. PV plants were simulated near communities in the southeast of the province (Brocket and Seven Persons), which has the strongest solar resource; regions close to existing coal plants (Verger, Evansburg, and Lacombe); and near large oil and gas industrial loads (Lindbergh and Mildred Lake); as well as in the less populated northern region (Teepee Creek and Keg River). Figure 4 shows the chosen locations of the hypothetical PV plants throughout Alberta, although choosing different permutations of these sites has minimal impact on the aggregate results. We employed solar data from Alberta Climate Information Service (ACIS) [59] and Environment and Climate Change Canada (ECCC) [60]. These two databases provided hourly solar direct horizontal irradiance, 10-meter-above-ground wind speed and direction, ambient dry bulb temperature, dew point, and atmospheric pressure. We used data from the National Solar Radiation Database (NSRDB) of the National Renewable Energy Laboratory (NREL) [61] to estimate any data missing from the ACIS and ECCC databases.

2.4. Economic, Environmental, and Energy Dispatch Metrics

As explained in the Methodology section, various economic and environmental metrics were calculated.

Table 1. Economic, environmental, and energy generation metrics are analyzed in this study.

Economic Metric	Environmental Metric	Generation Metric
Hourly price	Grid emission intensity	Load after PV energy is introduced
Market value	Displaced emissions per plant type after the introduction of PV energy	Displaced dispatch per plant type after the introduction of PV energy
Value factor

summarizes the analyzed metrics in this study:

3. Results

3.1. Pricing Dynamics Impacts on PV Price Cannibalization

This study analyzed data containing various pricing dynamics from a period when Alberta’s electricity market had little to no PV energy share, as illustrated in Figure 1. We categorized these years into three pricing regimes based on their mean hourly price levels following the procedure of Durán-Castillo et al. [42]:

• Low-priced years: 2016 and 2017, with average prices near 25 CAD/MWh;
• Medium-priced years: 2014, 2015, 2018, and 2019, with average prices around 50 CAD/MWh;
• High-priced years: 2011 to 2013, with average prices exceeding 100 CAD/MWh during solar generation hours.

These pricing regimes are referred to as “low”, “medium”, and “high” [42]. An analysis of the hourly price levels revealed that higher mean hourly prices were associated with steeper merit-order curve slopes [42], as depicted in Figure 2a,b. These regimes were visually differentiated in Figure 2 using blue (low), orange (medium), and green (high) for their respective pricing levels.

The simulations introduced hypothetical PV capacities into the market, modifying the merit order by adding PV generation at zero marginal cost. This led to price reductions primarily during daylight hours (approximately 7 am to 9 pm), which historically correspond to the highest-priced hours. The degree of price cannibalization and the emergence of a “duck curve” varied with the pricing regime and the level of PV penetration [42]. Figure 5 highlights the annual mean hourly price changes across the three pricing regimes.

• Introducing 1 GW of hypothetical PV capacity reduced annual mean hourly prices by the following amount:

  ○

  11% in low-priced years (e.g., 2017);

  ○

  25% in medium-priced years (e.g., 2019);

  ○

  46% in high-priced years (e.g., 2013).

• Increasing PV capacity to 3 GW resulted in even larger price reductions:

  ○

  17% in low-priced years;

  ○

  32% in medium-priced years;

  ○

  57% in high-priced years.

As PV energy displaced higher-priced offers during peak solar generation hours, the reductions in prices were more pronounced in high-priced regimes [42]. However, solar output declined in the evening as the sun set, potentially causing prices to rise again due to the dispatch of fast-response generators. This rebound effect was not modeled in this study.

The results demonstrated how pricing regimes and PV penetration levels interact to influence market dynamics, highlighting the challenges of integrating significant solar capacity into Alberta’s electricity market.

Historical annual normalized value factors of each type of plant in the fleet are shown in Figure 6, where it can be seen that SC peaking plants attain the highest market values, followed by the small amounts of solar PV energy in the system at the time. On the other hand, wind farms often generate off-peak times and cannibalize their market values below the pool price; this is known in Alberta as the “wind discount” [62].

The PV-induced hourly price cannibalization impacted the market value of each plant type. These results agreed with other works that have found that the revenue of traditional resources is affected by PV price cannibalization [22,42]. Figure 7 presents the market value variation per hypothetical PV installed capacity level. At low PV installed capacities, the hypothetical PV plant type had some of the highest market values in the fleet mix because the PV production during midday hours coincides with the hours with high electricity prices. After introducing a 1 GW PV capacity, the hypothetical PV power started to attain even lower market values than wind, which had the lowest market values during the studied years. The market value of PV dropped by 30%, 66%, and 78% with 1 GW of PV installed capacity in the low-, medium-, and high-price regimes, respectively, and as much as 51%, 75%, and 95% with 3 GW of PV installed capacity. These results align with previous studies that found less volatility when the merit-order slope is flatter because the price is less affected by price cannibalization [24]. As more zero-dollar PV energy was introduced, PV became the plant type with the lowest market value due to solar price cannibalization, also observed in other studies [19]. This work illustrates how the market value for different technologies can decline depending on the merit-order shape. At the same time, solar goes from the highest market value in all cases to the lowest one, illustrating the renewable energy policy paradox introduced by Blazquez et al. [21].

The steep cannibalization of market values in our simulation is caused by the “hourly generators’ offers” structure: a significant portion of the merit order is composed of zero-dollar priced dispatch, as seen in Figure 3, for one hour.

3.2. Impact of Batteries on Price Stabilization and Market Value

To study the effect of batteries on the stabilization of market prices after the introduction of PV, we simulated the introduction of 500 MW/1000 MWh on the impacted prices by the 3000 MW PV capacity. In the simulation, batteries were modeled to discharge at a marginal cost of 0 CAD/MWh, while paying the market price during charging hours. The batteries were programmed to charge during periods of the lowest market prices, thereby raising hourly prices by necessitating the dispatch of higher-priced generation. Conversely, the batteries were discharged during periods of the highest market prices, thereby reducing hourly prices by displacing higher-priced generation offers with zero-priced battery energy. Figure 8 presents the comparison of historical prices impacted by the 3000 MW PV capacity of hypothetical solar and 500 MW/1000 MWh capacity batteries. As shown, the integration of battery storage results in a flattening of daily hourly price profiles, with peak prices reduced and trough prices elevated relative to the PV-only scenario.

Furthermore, we calculated the market value for various plant types. Our analysis indicates that the market value of each plant type, across all price regimes (low, medium, and high), was the lowest when batteries were integrated into the system. This outcome is attributed to the battery discharging at 0 CAD/MWh during periods of the highest prices, thereby suppressing peak prices and reducing the market value of all generation types. Figure 9 presents the market value by price regime for each plant type. For batteries, the results showed that, in the low- and medium-price regimes, their market value was the lowest among all technologies. This is because price arbitrage—the strategy of purchasing electricity during periods of low prices and selling during periods of high prices [63]—was limited under low- and medium-price conditions, diminishing the profitability of battery operations. However, under high-price regimes, the extent of price arbitrage was sufficient to yield the highest market value for batteries relative to other plant types. The price arbitrage in the high-price regime was 28.29 CAD/MWh, compared to 18.31 CAD/MWh and 6.32 CAD/MWh in the medium- and low-price regimes, respectively.

Table 2. Annual price arbitrage of batteries in low-, medium-, and high-price regimes.

Price Regime	Charging Price (Buying) (CAD/MWh)	Discharging Price (Selling) (CAD/MWh)	Price Arbitrage (Selling Price−Buying Price) (CAD/MWh)
Low (2017)	14.53	20.85	6.32
Medium (2019)	21.94	40.25	18.31
High (2013)	10.87	39.16	28.29

presents the annual price arbitrage of batteries in the low-, medium-, and high-price regimes.

We observe that battery discharge exerts a similar effect on the merit order as the introduction of PV energy, as both sources dispatch electricity at a marginal cost of 0 CAD/MWh, thereby exerting downward pressure on market prices. However, while PV energy reduced prices specifically during periods of solar irradiance, battery energy lowered the highest daily prices regardless of the time of day.

The results highlight the variation in market price dynamics across different years and emphasize the distinct effects of battery charging and discharging on market price stabilization. As illustrated in Figure 8, the increase in hourly prices during battery charging periods was relatively modest across all price regimes, amounting to 2.47, 4.59, and 3.89 CAD/MWh for the low-, medium-, and high-price regimes, respectively. In contrast, the price reduction during battery discharge periods grew significantly from the low- to the high-price regime, with decreases of 2.06, 12.01, and 31.44 CAD/MWh, respectively.

These simulations offer valuable insights for non-VRE, non-battery market participants, who may adapt their bidding strategies in response to the price impacts of battery operations. One possible strategy could involve increasing offer prices during periods that historically exhibited lower prices to secure sufficient revenue. However, such strategic bidding behavior by non-VRE, non-battery resources could diminish the price arbitrage opportunities for batteries by raising the cost of charging. Given that the strategic adaptation of non-VRE, non-battery resources could adversely impact battery profitability, it becomes necessary to design policies that safeguard the role of batteries in maintaining system reliability, independent of wholesale market economics, which may otherwise hinder battery revenues by eroding price arbitrage opportunities.

Future advancements in energy storage and grid management technologies hold significant potential for mitigating the price cannibalization effects associated with increasing PV penetration. Our analysis shows that battery storage systems are themselves susceptible to price cannibalization, particularly when they discharge electricity during periods of historically high demand. Additionally, the charging of storage during times corresponding to the belly of the duck curve can contribute to upward pressure on market prices during midday hours. Our findings are consistent with those of Restel and Say [33], whose analysis demonstrated that time-varying export tariffs can influence the operational behavior of PV-BESS households. By increasing the flexibility in the timing of battery discharge, such tariffs encourage energy exports during periods of high demand and promote storage during times of excess supply. This behavior contributes to reshaping the net load curve, notably by reducing the depth of the midday trough of the PV-induced “duck curve”. Overall, dynamic pricing mechanisms and the coordination of distributed energy resources support a more economically and technically balanced integration of renewable energy, contributing to lower wholesale electricity prices and supply costs [33].

By utilizing strategic bidding by battery storage systems to enhance price arbitrage opportunities, batteries actively participate in electricity markets as price makers, influencing market prices during both charging and discharging intervals. By selectively withholding energy during low-price periods and releasing it during high-price hours, batteries can increase revenue generation [64]. Furthermore, the presence of demand elasticity—where electricity demand adjusts in response to price fluctuations—contributes to the formation of smoother and more stable price duration curves, particularly in power systems with high penetrations of VRE sources and energy storage technologies [64]. When integrated with strategic storage bidding, this approach further enhances the potential for investment recovery.

3.3. Changes to Dispatched Energy

In this section, we analyze the changes in system load resulting from the introduction of hypothetical PV generation profiles into the market. Given that technologies with higher marginal costs typically bid at correspondingly higher prices, this approach provides a reasonable approximation for identifying which generation technologies are displaced first and their associated emissions reductions. These effects are illustrated across three representative years in Figure 10. Unlike the previous section, which was organized according to merit-order characteristics, the results presented here follow a chronological order to highlight temporal patterns in the system response to solar integration.

The introduction of hypothetical PV generation results in the displacement of the highest-priced generating units. The methodology used to determine the displaced generation follows the approach described in Section 2.1 and illustrated in Figure 3a,b. Specifically, we identified the generating units removed from the merit order due to the zero-price PV introduction. The plant types corresponding to the displaced generation were then identified to assess the impact on the generation mix.

From 2010 to 2017, SC had the highest rate of displacement based on historical dispatch, followed by CC. In 2018, a more stringent carbon-pricing regime was implemented in Alberta, where emissions above 0.37 t_CO2e/MWh were priced at 30 CAD/t_CO2e [65,66,67]. This resulted in an approximately 18.9 CAD/MWh emission cost for a typical coal unit, compared to around 4.9 CAD/MWh for SC units and minimal (or potentially even negative) costs for high-efficiency CC units, which have emissions rates close to the 0.37 t_CO2e/MWh benchmark. As a result, for both 2018 and 2019, coal increasingly became a higher-priced offer and was displaced more than any other technology. Figure 11 and Figure 12 show the dispatch displacement per plant type and PV installed capacity in percentage and absolute terms.

While Alberta’s demand profile is relatively flat overall, it has a winter peak when solar PV energy is least available or unavailable. While demand peaks and solar output align well in the summer, on average, throughout the year, the peak demand occurs around the time when solar generation is decreasing. Much of Alberta’s flexible generation is fossil-fuel-based and would need to ramp up to cover the gap in demand between decaying PV generation and rising energy consumption.

3.4. Greenhouse Gas Emissions Displacement

Before 2018, Alberta’s Specified Gas Emitters Regulation (SGER) obliged industrial emitters with annual greenhouse gas (GHG) emissions of 100,000 t_CO2e or more to meet annual emissions intensity reductions. Electricity generators were required to reduce their GHG emissions per MWh by 12% compared to an established baseline year or pay a cost for emissions above this threshold. Facilities failing to make these reductions could seek emissions credits or pay into a carbon fund at a price starting at 15 CAD/t_CO2e in 2007. It has varied per year as follows: 15 CAD/t_CO2e from 2007 to 2015, 20 CAD/t_CO2e in 2016, 30 CAD/t_CO2e from 2017 to 2020, 40 CAD/t_CO2e in 2021, and 50 CAD/t_CO2e in 2022; to then increase in 15 CAD/t_CO2e until reaching 170 CAD/t_CO2e in 2030 [68,69]. In 2018, the Carbon Competitiveness Incentive Regulation (CCIR) replaced the SGER and set the emission intensity limit to a benchmark for the electricity sector of 0.37 t_CO2e/MWh instead of individual facility-based limits [65,66,67]. While Alberta’s electricity emissions intensity did not change significantly from 2010 to 2017, it began to decrease in 2018 as coal facilities began offering higher prices due to CCIR and, in some cases, retired. The Technology Innovation and Emissions Reduction Regulation (TIER) replaced the CCIR in 2019 but retained the output-based benchmark of 0.37 t_CO2e/MWh.

We found that the displacement of emissions from coal-fired generation (with the same PV energy) increased from 2010 to 2019. In 2010, 6.1% of the coal-fueled generation was displaced, while this value increased to 10.6% in 2019. On the contrary, in 2010, 30.2% of the original SC generation was displaced, while this value decreased to 6.9% in 2019. These results show the effect of carbon pricing as it increases the likelihood that the highest emitting generation sources get to be at the margin of the merit order and, thus, be displaced first. Figure 13 shows the percentage of emissions reduced per plant type and the hypothetical PV capacity.

Alberta’s electricity system grew significantly over the study period, increasing from 66.1 TWh/yr in 2010 to 77.3 TWh/yr by the beginning of 2020. Much of the increase in generation came from natural gas and wind, both with lower GHG emissions rates than coal, and, as a result, the GHG emission intensity declined, while the absolute emissions increased from 50.1 Mt_CO2e in 2010 to a peak of 54.2 Mt_CO2e in 2014; the average grid emissions intensity decreased significantly from 0.76 to 0.56 t_CO2e/MWh from 2010 to 2019.

Table 3. Annual energy and emissions before and after PV introduction in the system.

Year	Total Market Energy (TWh)	Annual Emission Intensity (tCO2e/MWh)	Total Market Emissions (MtCO2e)	Hypothetical PV Capacity (GW)	Displaced Emissions Intensity (tCO2e/MWh)	Hypothetical Displaced Emissions (MtCO2e)
2010	66.1	0.76	50.1	1	0.72	1.00
				3	0.77	3.19
2015	74.5	0.70	52.0	1	0.80	1.15
				3	0.85	3.67
2019	77.3	0.56	43.3	1	0.87	1.25
				3	0.77	3.32

lists the annual mean rate of CO₂e emissions displacement for each year in the study period.

In 2015, an 860 MW CC plant was commissioned, and both emissions intensity and absolute emissions decreased as the lower-emitting gas plant displaced higher marginal coal unit offers. By 2021, annual GHG emissions had decreased to 38.2 (Mt_CO2e) as coal units began to convert to natural gas, reduced their capacity factors, or retired altogether. The total displaced emissions by the hypothetical solar energy differed yearly, as seen in Table 1. While the total annual emissions reductions could be significant, the change in overall emissions intensity varies from hour to hour and can be seen in Figure 14.

Throughout the study period, coal was the largest source of emissions reductions. Figure 15 illustrates the displaced generation and emissions in 2010 and 2019 with the introduction of the 1 GW and 3 GW PV capacity, respectively. Despite the overall grid intensity reduced by close to 20% from 2015 to 2019, hypothetical solar units’ CO_2e emissions savings did not drop by the same amount, instead, only declining by 9% from their 2015 levels for the 3 GW PV capacity. This can be attributed to an increase in the relative proportion of coal offering higher prices as the effective carbon price increased from approximately 1.8 CAD/MWh in 2015 to 18.9 CAD/MWh in 2019 for a coal plant with a 1.0 t_CO2e/MWh emissions intensity.

3.5. Comparison with the Literature

As jurisdictions transition from coal to lower-carbon energy sources, both empirical observations and modeling studies have revealed persistent economic and operational challenges in electricity markets with rising shares of VRE, particularly solar PV. A prominent example is California’s “duck curve”, identified by CAISO in 2013 [16], where high solar penetration substantially depresses midday electricity prices. Similar patterns have been documented globally: Germany has experienced significant merit-order effects from wind and solar, resulting in increased volatility [23]; in Italy [22], VRE integration has altered price formation and affected generators’ market value; and Australia’s South West Interconnected System (SWIS) exhibits comparable duck curve dynamics [17].

The modeling work by Denholm et al. [14] further illustrates how increasing PV penetration displaced thermal generation in the Western Electricity Coordinating Council (WECC) region. At low levels of PV, gas-fired plants were predominantly displaced; however, once PV reached a threshold of approximately 4% of total generation, coal and other baseload units began to be offset as well. The effect is regionally specific: in Colorado, coal displacement intensified with PV growth, whereas, in California, displacement shifted toward electricity imports. These findings highlight how the composition of regional generation fleets shapes the decarbonization trajectory and capacity utilization.

Complementing these operational insights, López Prol et al. [70] provided econometric evidence of the revenue impacts of VRE in California’s market. Their analysis showed that solar PV suffers more severe revenue cannibalization than wind, with each additional percentage point of solar penetration reducing revenues by 1.295 USD/MWh. Importantly, the study identified asymmetric cross-cannibalization: while wind eroded solar’s value factor, solar increased wind’s relative market value—particularly due to post-sunset price spikes. These dynamics underscore how VRE technologies not only face declining marginal revenues but also interact in complex, non-linear ways within competitive electricity markets.

Further, Restel and Say [33] investigated the effects of retail electricity tariff structures on household PV-BESS behavior in Melbourne, Australia. The study found that time-varying export tariffs incentivized battery discharge during peak evening hours (when prices are high) and reduced grid feed-in during midday (when prices are low), thereby reshaping the duck curve by mitigating its depth and smoothing its peak.

Although Alberta’s electricity market has distinctive characteristics—such as its energy-only design and substantial cogen capacity—the fundamental economic and environmental dynamics observed in this analysis, particularly price cannibalization, emissions displacement, and the renewable energy paradox, are structurally analogous across deregulated markets undergoing energy transitions. This study also found that battery storage contributed to price stabilization but simultaneously reduced the overall market value for all generators. These findings underscore the risk that, in markets lacking storage incentives or mechanisms for strategic bidding by storage operators, limited price arbitrage opportunities may discourage investment in storage technologies.

In jurisdictions such as Alberta, the interplay between carbon pricing and PV penetration significantly influences which fossil fuel units are displaced and how emissions trajectories evolve. By calibrating the model with jurisdiction-specific merit-order data, the methodology and conclusions presented here are broadly transferable and scalable to other liberalized electricity markets with increasing VRE penetration.

3.6. Discussion

Our study examined the years before significant PV energy was introduced into the Alberta market. By 2022, over 1000 MW of PV installed capacity was available in Alberta’s market, accounting for just over 2% of the annual energy generation. While in other markets, there was price cannibalization at times when PV energy was dispatched, such as in California [18] and Italy [22], in Alberta, prices increased in 2021 and 2022 despite the growth of solar PV energy. This was likely due to other market factors, including increases in natural gas prices in part due to the conflict in Ukraine, which also saw European electricity prices increase with higher-cost natural-gas-fueled generators [71].

A more significant effect, however, was likely due to a concentration in market power in these years, muting the merit-order effect, as companies were allowed to economically withhold generation in Alberta’s market [72,73]. How the future market will operate depends on the generators’ bidding behavior and any future market rule changes that may aim to balance generator revenue with system reliability [74]. Ciarreta et al. discussed that conventional generators typically engage in three possible bidding strategic behaviors [75]. The first would be reducing the bidding price to ensure their generation is not displaced from the merit order. The second could be that generators refuse to participate in the merit order as their production cost may be higher than the renewable-energy-induced pool clearing price. Lastly, some generators could shift their operating hours to times with less VRE, allowing them to sell their output without competing with zero-dollar-marginal-price wind and solar energies. Alberta’s minimum mean hourly price of non-zero-dollar offers was found to have increased from around 5 CAD/MWh between 2009 and 2019 to 20 CAD/MWh in 2022. Depending on the year, the zero-dollar dispatch portion varied from the lowest of 75% in 2017 to the highest of 84% in 2022. Figure 16 presents the actual merit order in two hours, one from 2022 and the other from 2019, illustrating the jump in the minimum non-zero-dollar dispatch.

The principal factors that have impacted the prices historically in Alberta are the supply cushion, the gas price, the carbon price, the market power, the weather, the planned and forced thermal outages, transmission outages, the imports/exports, and the year-over-year demand [73,76,77,78,79]. Market power is exerted by large suppliers that offer large generation volumes at high prices, pushing an overall increase in the hourly market price, especially if there is high demand and a low supply cushion [79]. The supply cushion refers to the entire undispatched generation offered in the merit order; a decline in the supply cushion is associated with higher prices [80]. In 2022, the prices were high due to the natural gas price, carbon price, large export volumes to Mid-Columbia and California, and a concentration of market power in readily dispatchable units [73,77,78,79]. On the other hand, in 2016, the mean hourly price was particularly low due to a large number of low-priced offers, a high supply cushion, low natural gas prices, and a drop in year-over-year demand [72].

Surplus supply will override market participants’ ability to offer higher prices strategically. Supply surplus refers to a zero-dollar electricity supply greater than the demand. System operators may need mitigation techniques such as curtailment, cutting in-merit power, re-scheduling exports, or cutting off imports [81]. There were 68 supply surplus events in 2020, but only 2 in each of 2021 and 2022, when market prices dropped to 0 CAD/MWh. However, by the beginning of 2023, as more solar has come onto the system, the emergence of a duck curve has developed as expected.

3.7. Limitations of This Analysis

This research does not address the reliability challenges caused by the entrance of VRE due to the changes in power flow patterns and retirement of coal and natural gas synchronous generators [82,83]. One of the grid’s reliability challenges is the frequency response, which refers to stabilizing the system’s frequency after a mismatch of generation and load [84]. The electricity system is designed to provide a frequency response to counteract the outage of the largest generator [83,84]. VRE does not inherently contribute to regulating the grid frequency unless equipped with controls and special operation [84]. The Alberta Electric System Operator (AESO) has found system strength challenges in local areas but not system-wide; areas with a geographic concentration of VRE in southern Alberta are estimated to become weak areas [83]. Investments in smart grid projects include battery energy storage, advanced distribution control, and conservation voltage reduction software technology [85]. The voltage reduction software adjusts the voltage to connected consumers and controls the active and reactive power, supporting grid stability [86]. The control of generation and frequency regulation is indispensable in smart power grids [87]. Energy storage can participate in frequency response due to its ability to charge and discharge electricity [88]. Alberta’s battery energy storage installed capacity is growing from 190 MW in 2025 [89] to a projected 868 MW related to 20 energy storage projects in AESO’s connection list [90].

We do not address transmission constraints such as congestion, failures, and costs. Transmission congestion is related to transmission overload due to the volume of loads and generation [88]. The transmission system planning identifies possible reliability challenges and considers future generic and specific generation developments [88]. The transmission system is planned to serve and facilitate 95% of the electricity market transactions without congestion [91].

Moreover, we do not investigate the annual revenues necessary to attain the recovery of capital, and fixed and variable operation and maintenance. The cost recovery of power plants is their own risk in the energy-only market. Our analysis shows the changes in the generators’ market value in a market that historically has varied its price, merit-order behavior, and minimum non-zero-dollar offer value from year to year.

4. Conclusions and Policy Implications

This investigation considered the potential consequences of the rapid introduction of PV energy into a highly thermal fleet. We calculated the hypothetical duck curve for the price and load in a market transitioning from a coal-based fleet mix to a gas and VRE mix. Our results are to be considered in policy design in order to achieve a fleet mix that complies with environmental and energy security goals.

The increasing levels of PV capacity had effects on hourly electricity prices, emissions reductions, and the broader dynamics of electricity markets. As PV penetration rose, the influx of zero-marginal-cost PV energy during daylight hours displaced higher-cost fossil-fueled generation, particularly during midday periods. This led to a suppression of wholesale electricity prices—a phenomenon known as the merit-order effect. This results in what is commonly referred to as “price cannibalization” where the increment of PV lowers the average market price during hours when PV is most productive, thereby reducing the market value and revenue that PV and other generators can capture from the market.

From an environmental perspective, the integration of PV initially yields emissions savings by displacing coal- and natural-gas-fueled generation. However, the marginal emissions reduction per unit of PV added declines as cleaner resources are displaced.

At a system level, these changes in dispatch and pricing dynamics affect investment signals across the generation fleet. Lower and more volatile energy prices erode the profitability of both PV and conventional generators.

In Alberta, we found that the years that presented higher hourly prices also showed a steeper merit-order slope and smaller non-zero-dollar dispatch portion, resulting in larger price cannibalization and, thus, a more significant drop in market values. In the simulation, introducing PV could diminish market prices more significantly than the load displaced. For example, with 1 GW of PV capacity, the load and emissions at the “duck’s belly” (midday hours) decreased by up to 7% and 9%, respectively, while hourly prices dropped by approximately 27%, 58%, and 81% under the low, medium, and high pricing regimes. At 3 GW of PV capacity, the hourly prices during belly hours declined further, by an average of 53%, 78%, and 95%, under the same pricing dynamics, while the load and CO₂ equivalent emissions decreased by only about 20% and 24%, respectively. These PV capacities—1 GW and 3 GW—represented approximately 2% and 6% of the system’s annual generation. This highlights the significant market price impacts of PV penetration compared to the modest reductions in load and emissions, which substantially affect the market value of solar energy.

Carbon pricing had a complementary effect of reducing high-emitting resources from the market and increasing the likelihood of their displacement by introducing zero-bidding VRE, such as solar. In 2018, a carbon-pricing regime was introduced where emissions above 0.37 t_CO2e/MWh were priced at 30 CAD/t_CO2e [65,66,67], resulting in an approximately 18.9 CAD/MWh cost for a typical coal unit, compared to around 4.9 CAD/MWh for SC units, which have emissions rates close to the 0.37 t_CO2e/MWh benchmark. As a result, for both 2018 and 2019, coal increasingly became a higher-priced offer and was displaced more than any other technology.

Strategic offers can delay the onset of price cannibalization by increasing the minimum mean hourly price and the slope of non-zero-dollar offers. The minimum annual mean hourly price of the non-zero-dollar offers grew from 3.19 to 20.02 CAD/MWh from 2010 to 2022. However, this ability is diminished when there is less market control and higher levels of VRE that result in hours close to or reaching supply surpluses. By the beginning of 2023, as more solar has come onto the system, a duck curve emerged as expected.

The integration of 500 MW/1000 MWh of battery storage alongside 3000 MW of PV capacity significantly stabilizes market prices by flattening the daily price duck curve, raising trough prices during charging, and lowering peak prices during discharging. This dynamic reduces overall price volatility but also leads to a decline in the market value of all generation types, as battery discharges at zero marginal cost during high-price hours. While batteries have limited price arbitrage in the low- and medium-price regimes, under the high-price regime, the arbitrage gains are more substantial. The price-suppressing effect of batteries mirrors that of PV, although batteries target peak-price periods irrespective of PV output.

The following policy recommendations are aimed at fostering an investment environment conducive to reducing greenhouse gas emissions during the transition to a cleaner electricity generation mix. First, feed-in tariff (FiT) remuneration schemes can be structured to provide revenue certainty for VRE projects through guaranteed fixed payments per megawatt-hour. These payments should be calibrated based on project-specific attributes such as the technology type, scale, and regional resource quality [92]. Second, battery storage systems could be enabled to engage in strategic bidding, allowing them to act as price makers by planning their charging and discharging schedules to enhance price arbitrage opportunities. This strategy enhances revenue stability for storage systems, particularly in low- and medium-price environments where arbitrage margins are typically constrained, thus supporting their investment recovery [64]. Third, we recommend implementing demand-side response programs so that consumers can adjust their electricity consumption in response to the real-time market price. By increasing demand elasticity, such programs contribute to market price stabilization and improve the investment climate for both storage technologies and VRE integration [64].