Search Results (51)

Interactions between the increased penetration of the power system by renewable energy sources (RESs) and the energy pricing mechanism in the EU (day-ahead market) can lead to many unexpected and paradoxical consequences. This article analyses the case of the long-term maintenance of prices around zero on the day-ahead market in south-western Europe at a certain time of a day. This is an important case since, at the same time, this area generates electricity from a similar source mix as it is in the target for the EU. Zero or very low energy prices are becoming increasingly common across the EU. This can pose a problem for the stability of the electricity supply, as it translates into a lower power of used disposable power sources, which can be used as a reserve when the majority of the energy supply comes from renewable energy sources. Furthermore, this work refutes the most frequently proposed solution to the problem of excessively low prices based on energy storage systems. This work attempts to analyze the long-term low-price situation in Spain and extrapolate the expected consequences based on it; however, it is difficult to find all the factors that occur in the power system and influence the price market and vice versa. The issue is multidimensional and complex, and the analyzed situation revealed a number of trends. Therefore, a multifaceted problem remains. A constant electricity supply must be ensured at a reasonable price, thus avoiding the exposure of individual consumers to energy shortages or significant price increases, while, at the same time, the EU must reduce dependence on fossil fuels, and its legislation must push for reduced CO₂ emissions. On the other hand, the EU must provide some type of market mechanism to support the achievement of these goals because the current pricing mechanism based on the day-ahead market does not seem to be effective. This article aims to spark a discussion about this problem; it does not provide any simple solutions to it. Full article

The increasing integration of renewable energy sources necessitates efficient energy storage solutions, with large-scale battery energy storage systems (BESS) playing a key role in grid stabilization and time-shifting of energy. This study presents a multi-level simulation framework for optimizing BESS operation across multiple markets while incorporating degradation-aware dispatch strategies. The framework integrates a day-ahead (DA) dispatch level, an intraday (ID) dispatch level, and a high-resolution simulation level to accurately model the impact of operational strategies on state of charge and battery degradation. A case study of BESS operation in the German electricity market is conducted, where frequency containment reserve provision is combined with DA and ID trading. The simulated revenue is validated by a battery revenue index. The study also compares full equivalent cycle (FEC)-based and state-of-health-based degradation models and discusses their application to cost estimation in dispatch optimization. The results emphasize the advantage of using FEC-based degradation costs for dispatch decision-making. Future research will include price forecasting and expanded market participation strategies to further improve and stabilize the profitability of BESS in multi-market environments. Full article

With the soaring demand for electric power and the limited spinning reserve in the power system in Taiwan, the comprehensive management of both thermal power generation and load demand turns out to be a key to achieving the robustness and sustainability of the power system. This paper aims to design a demand bidding (DB) mechanism to collaborate between customers and suppliers on demand response (DR) to prevent the risks of energy shortage and realize energy conservation. The concurrent integration of the energy, transmission, and reserve capacity markets necessitates a new formulation for determining schedules and marginal prices, which is expected to enhance economic efficiency and reduce transaction costs. To dispatch energy and reserve markets concurrently, a hybrid approach of combining dynamic queuing dispatch (DQD) with direct search method (DSM) is developed to solve the extended economic dispatch (ED) problem. The effectiveness of the proposed approach is validated through three case studies of varying system scales. The impacts of tie-line congestion and area spinning reserve are fully reflected in the area marginal price, thereby facilitating the determination of optimal load reduction and spinning reserve allocation for demand-side management units. The results demonstrated that the multi-area bidding platform proposed in this paper can be used to address issues of congestion between areas, thus improving the economic efficiency and reliability of the day-ahead market system operation. Consequently, this research can serve as a valuable reference for the design of the demand bidding mechanism. Full article

The growing investment in variable renewable energy sources is changing how electricity markets operate. In Europe, players rely on forecasts to participate in day-ahead markets closing between 12 and 37 h ahead of real-time operation. Usually, transmission system operators use a symmetrical procurement of up and down secondary power reserves based on the expected demand. This work uses machine learning techniques that dynamically compute it using the day-ahead programmed and expected dispatches of variable renewable energy sources, demand, and other technologies. Specifically, the methodology incorporates neural networks, such as Long Short-Term Memory (LSTM) or Convolutional neural network (CNN) models, to improve forecasting accuracy by capturing temporal dependencies and nonlinear patterns in the data. This study uses operational open data from the Spanish operator from 2014 to 2023 for training. Benchmark and test data are from the year 2024. Different machine learning architectures have been tested, but a Fully Connected Neural Network (FCNN) has the best results. The proposed methodology improves the usage of the up and down secondary reserved power by almost 22% and 11%, respectively. Full article

Despite the massive increase of renewable energy generation in Greece, large-scale battery energy storage systems (BESS) are yet to be integrated in the Greek electricity market. This paper analyzes the profitability of BESS in Greece, focusing on the Day-Ahead Market (DAM) and the Frequency Containment Reserve (FCR) market. To this end, we examine and compare the following three instances of BESS market participation with respect to the short-term uncertainty BESS participants face in terms of market prices and FCR utilization factors: (a) a theoretical perfect information instance, (b) a deterministic instance based on average historical values of the uncertain parameters, and (c) a stochastic instance based on alternative scenarios stemming from historical data. The last two instances are complemented by an out-of-sample validation representing BESS operation after uncertainty is materialized. Furthermore, for each of these three instances, we explore three cases involving participation only in the DAM, only in the FCR market, and in a combination of the DAM and FCR market, accounting for the different pricing mechanisms of these markets. The case studies employ real market and frequency data from Greece and compare the three instances and three market participation cases in terms of achieved profit and energy violation rate. Full article

This paper introduces a mean profit- conditional value-at-risk (CVaR) model for purchasing electricity on the day-ahead market (DA) by electric vehicles fleet aggregator (EVA). EVA controls electric vehicles (EVs) during their workplace parking, enabling smart charging and cost savings by accessing market prices that are potentially lower than flat rates available during home charging. The proposed stochastic linear programming model leverages market price scenarios to optimize aggregated charging schedules, which serve as templates for constructing effective DA bidding curves. It integrates an aspiration/reservation-based formulation of the mean profit-risk criteria, specifically Conditional Value at Risk (CVaR) to address the EVA’s risk aversion. By incorporating interactive analysis, the framework ensures adaptive and robust charging schedules and bids tailored to the aggregator’s risk preferences. Its ability to balance profitability with risk is validated in case studies. This approach provides a practical and computationally efficient tool for EV aggregators of global companies that can benefit from the workplace charging their fleets thanks to buying energy in the DA market. Full article

An Energy Hub (EH) is able to manage several types of energy at the same time by aggregating resources, storage devices, and responsive loads. Therefore, it is expected that energy efficiency is high. Hence, the optimal operation for smart EHs in energy (gas, electrical, and thermal) networks is discussed in this study based on their contribution to reactive power, the energy market, and day-ahead reservations. This scheme is presented in a smart bi-level optimization. In the upper level, the equations of linearized optimal power flow are used to minimize energy losses in the presented energy networks. The lower level considers the maximization of profits of smart EHs in the mentioned markets; it is based on the EH operational model of resource, responsive load, and storage devices, as well as the formulation of the reserve and flexible constraints. This paper uses the “Karush–Kuhn–Tucker” method for single-level model extraction. An “unscented transformation technique” is then applied in order to model the uncertainties associated with energy price, renewable energy, load, and energy consumed in mobile storage. The participation of hubs in the mentioned markets to improve their economic status and the technical status of the networks, modeling of the flexibility of the hubs, and using the unscented transformation method to model uncertainties are the innovations of this article. Finally, the extracted numerical results indicate the proposed model’s potential to improve EHs’ economic and flexibility status and the energy network’s performance compared to their load flow studies. As a result, energy loss, voltage, and temperature drop as operation indices are improved by 14.5%, 48.2%, and 46.2% compared to the load flow studies, in the case of 100% EH flexibility and their optimal economic situation extraction. Full article

The integration of photovoltaic and electric vehicles in distribution networks is rapidly increasing due to the shortage of fossil fuels and the need for environmental protection. However, the randomness of photovoltaic and the disordered charging loads of electric vehicles cause imbalances in power flow within the distribution system. These imbalances complicate voltage management and cause economic inefficiencies in power dispatching. This study proposes an innovative economic strategy utilizing battery energy storage system and electric vehicles cooperation to achieve voltage regulation in photovoltaic-connected distribution system. Firstly, a novel pelican optimization algorithm-XGBoost is introduced to enhance the accuracy of photovoltaic power prediction. To address the challenge of disordered electric vehicles charging loads, a wide-local area scheduling method is implemented using Monte Carlo simulations. Additionally, a scheme for the allocation of battery energy storage system and a novel slack management method are proposed to optimize both the available capacity and the economic efficiency of battery energy storage system. Finally, we recommend a day-ahead real-time control strategy for battery energy storage system and electric vehicles to regulate voltage. This strategy utilizes a multi-particle swarm algorithm to optimize economic power dispatching between battery energy storage system on the distribution side and electric vehicles on the user side during the day-ahead stage. At the real-time stage, the superior control capabilities of the battery energy storage system address photovoltaic power prediction errors and electric vehicle reservation defaults. This study models an IEEE 33 system that incorporates high-penetration photovoltaics, electric vehicles, and battery storage energy systems. A comparative analysis of four scenarios revealed significant financial benefits. This approach ensures economic cooperation between devices on both the user and distribution system sides for effective voltage management. Additionally, it encourages trading activities of these devices in the power market and establishes a foundation for economic cooperation between devices on both the user and distribution system sides. Full article

The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront, and the research interest in microgrids that rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper builds on the existing research framework by combining PPO with machine learning-based load forecasting to produce an optimal solution for an industrial microgrid in Norway under different pricing schemes, including day-ahead pricing and peak pricing. It addresses the peak shaving and price arbitrage challenges by taking the historical data into the algorithm and making the decisions according to the energy consumption pattern, battery characteristics, PV production, and energy price. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared with logic-based optimization. These findings contribute to digitalization and decarbonization of energy technology, and support the fundamental goals and policies of the European Green Deal. Full article

This study examines the profitability and reliability of a virtual power plant (VPP) with the existence of a diesel genset (DG) in the day-ahead (DA) and intra-day (ID) power markets. The study’s unique contribution lies in integrating the VPP system with non-spinning reserve DG while limiting the DG operation via minimum running time and maximum number of switching times (on/off) per day. This contribution decreases the renewables’ uncertainty and increases the VPP’s reliability. Moreover, the study proposes an optimization model as a decision-making support tool for power market participants to choose the most profitable short-term market. The proposed model suggests choosing the DA market in 62% of time (from 579 days) based on estimated VPP power supply, and market prices. Even though there is uncertainty about VPP power supply and market prices, the division between the plan and actual profits is 1.8 × 10⁶ Japanese yen [JPY] per day on average. The share of surplus power sold from the mentioned gap is 5.5%, which implies the opportunity cost of inaccurate weather forecasting. The results also show that the reliability of the VPP system in the presence of a DG increases from 64.9% to 66.2% for 14 h and mitigates the loss of power load by 1.3%. Full article

Large-scale fluctuating and intermittent new energy power generation in a new power system is gradually connected to the grid. In view of the impact of the uncertainty of wind power on the spinning reserve capacity of thermal power units in the new power system’s day-ahead dispatching and reserve auxiliary service market, the original dispatching mode and intensity can no longer meet the system demand. To address this problem, the establishment of a wind power grid-connected new power system’s standby auxiliary service market reward and punishment assessment mechanism is undertaken to fundamentally reduce the demand for auxiliary services of the new power system pressure. In the first part of this paper, a two-stage optimal scheduling strategy is proposed for the first day of the year that takes into account the operational risk and standby economics. First, a data-driven method is used to generate the forecast value of the wind power interval before the day, and a unit start–stop optimization model (the first-stage optimization model) is established by taking into account the CvaR (conditional value at risk) theory to optimize the risk loss of wind abandonment and loss of load and the fuel cost of each unit, and an optimization algorithm is used to carry out the three scenarios and the corresponding four scenarios to optimize the configuration of the start–stop state and power output of each unit. The optimization algorithm is used to optimize the starting and stopping status and output of each unit for three circumstances and four corresponding scenarios. Then, in the second stage, a standby auxiliary service market incentive and penalty assessment model is established to effectively coordinate the sharing of rotating standby capacity and cost among thermal power units through the incentive and penalty mechanism so as to make a reasonable and efficient allocation of wind power output, curtailable load, and synchronized standby capacity. The new power system with improved IEEE30 nodes is simulated and verified, and it is found that the two-stage optimization model obtains a scheduling strategy that takes into account the system operating cost, standby economy, and reliability, and at the same time, through the standby auxiliary service market incentive and penalty assessment mechanism, the extra cost caused by standby cost mismatch can be avoided. This evaluation model provides a reference for the safe, efficient, flexible, and nimble operation of the new power system, improves the economic efficiency and improves the auxiliary service market mechanism. Full article

In recent years, with the rapid increase in renewable energy sources (RESs), a Virtual Power Plant (VPP) concept has been developed to integrate many small-scale RESs, energy storage systems (ESSs), and customers into a unified agent in the electricity market. Optimal coordination among resources within the VPP will overcome their disadvantages and enable them to participate in both energy and balancing markets. This study considers a VPP as an active agent in reserve provision with an upward reserve capacity contract pre-signed in the balancing capacity (BC) market. Based on the BC contract’s requirements and the forecasted data of RESs and demand, a two-stage stochastic optimization model is presented to determine the VPP’s optimal scheduling in the day-ahead (DA) and balancing energy (BE) markets. The probability of reserve activation in the BE market is considered in this model. The ESS’s reserve provision model is proposed so as not to affect its schedule in the DA market. The proposed optimal scheduling model is applied to a test VPP system; then, the effects of the BC contract and the probability of reserve activation on the VPP’s trading schedule are analyzed. The results show that the proposed model has practical significance. Full article

In recent years, the escalating electricity demand in Taiwan has heightened the prominence and discourse surrounding the issue of power supply. With the enactment of the European climate law, global commitment to achieving net-zero emissions has gained momentum. Concurrently, the Taiwanese government has articulated the Taiwan 2050 net-zero emissions policy. To realize this objective, Taiwan has vigorously promoted renewable energy in recent years, increasing the proportion of renewable energy in its energy mix. However, confronted with the intermittent and unpredictable nature of renewable energy generation, challenges arise concerning the stability and quality of power supply. In response to the impact of integrating renewable energy into the grid, the Taiwan Power Company (Taipower) has introduced the day-ahead ancillary service market. Through this platform, power generation and battery energy storage systems (BESSs) engage in competitive bidding, fostering the integration of diverse distributed energy resources into the system. These resources serve as dispatchable assets for Taipower, contributing to grid stability by providing ancillary services. This study has developed an advanced dynamic regulation reserve controller by employing an industrial PC (IPC) in conjunction with a multifunctional power meter. Through real-time measurement of the power grid frequency by the multifunctional power meter, a sophisticated dynamic frequency reserve control strategy has been devised, taking into account the charging state of energy storage systems. This strategy exhibits high operational quality, effectively regulating the charging and discharging of energy storage systems. In addition to swiftly adjusting the current grid frequency, it encompasses the capability to facilitate the transfer of peak electrical energy. This effectively stabilizes the power grid, alleviating supply pressures during nighttime peaks and enhancing the overall supply stability of the power system. Full article

Forecasts and their corresponding optimized operation plans for energy plants never match perfectly, especially if they have a horizon of several days. In this paper, we suggest a concept to cope with uncertain load forecasts by reserving a share of the energy storage system for short-term balancing. Depending on the amount of uncertainty in the load forecasts, we schedule the energy system with a specific reduced storage capacity at the day-ahead market. For the day of delivery, we examine the optimal thresholds when the remaining capacity should be used to balance differences between forecast and reality at the intraday market. With the help of a case study for a simple sector-coupled energy system with a demand for cooling, it is shown that the energy costs could be reduced by up to 10% using the optimal reserve share. The optimal reserve share depends on the forecast quality and the time series of loads and prices. Generally, the trends and qualitative results can be transferred to other systems. However, of course, an individual evaluation before the realization is recommended. Full article

In pursuit of Finland’s carbon neutrality objective by 2035, integrating renewable energy sources into the power grid is essential. To address the stochastic nature of these resources, additional sources of flexibility are required to maintain grid stability. Meanwhile, district heating network (DHN) operators in Finland are decommissioning fossil fuel-based combined heat and power plants (CHPs) and electrifying heating systems with heat pumps (HPs) and electric boilers. A techno-economic assessment and the optimized operation of DHN-connected HPs and electric boilers in providing ancillary balancing services were explored in this study. The primary goal was to maximize the potential revenue for DHN operators through participation in the day-ahead electricity market and frequency containment reserve (FCR) balancing markets. Three interconnected DHNs in the Helsinki metropolitan area were optimized based on 2019 data and each operator’s decarbonization strategies for 2025. HPs are expected to achieve the highest profit margins in the FCR-D up-regulation market, while electric boilers could generate substantial profits from the FCR-D down-regulation market. In contrast to other balancing markets studied, the FCR-N market exhibited limited profit potential. Sensitivity analysis indicated that spot electricity prices and CO₂ emission allowance prices significantly influence the profitability derived from balancing markets. Full article