Search Results (115)

Standalone photovoltaic (PV) systems offer a viable path to decentralized energy access but face limitations during periods of low solar irradiance. While batteries provide short-term storage, their capacity constraints often restrict the use of surplus energy, highlighting the need for long-duration solutions. Green hydrogen, generated via proton exchange membrane (PEM) electrolyzers, offers a scalable alternative. This study proposes a stochastic energy management framework that leverages a Markov decision process (MDP) to coordinate PV generation, battery storage, and hydrogen production under variable irradiance and uncertain load demand. The strategy dynamically allocates power flows, ensuring system stability and efficient energy utilization. Real-time weather data from Goiás, Brazil, is used to simulate system behavior under realistic conditions. Compared to the conventional perturb and observe (P&O) technique, the proposed method significantly improves system performance, achieving a $99.9\%$ average efficiency (vs. 98.64%) and a drastically lower average tracking error of 0.3125 (vs. 9.8836). This enhanced tracking accuracy ensures faster convergence to the maximum power point, even during abrupt load changes, thereby increasing the effective use of solar energy. As a direct consequence, green hydrogen production is maximized while energy curtailment is minimized. The results confirm the robustness of the MDP-based control, demonstrating improved responsiveness, reduced downtime, and enhanced hydrogen yield, thus supporting sustainable energy conversion in off-grid environments. Full article

Despite the growing application of storage for curtailment mitigation, its cost-effectiveness remains uncertain. This study evaluates the Levelized Cost of Storage, which also represents an implicit threshold revenue, for Lithium-ion Battery Energy Storage Systems deployed for photovoltaic curtailment mitigation. Specifically, the LCOS is assessed—using a mathematical simulation model—for various curtailment scenarios defined by maximum levels (10–40%), hourly profiles (upper limit and proportional), and growth rates (2, 5, and 10 years) at three storage system capacities (0.33, 0.50, 0.67 h) and two European locations (Cagliari and Berlin). The results indicate that the LCOS of batteries deployed for curtailment mitigation is, on average, comparable to that of systems used for bulk energy storage applications (155–320 EUR/MWh) in Cagliari (180–410 EUR/MWh). In contrast, in Berlin, the lower and more variable photovoltaic generation results in significantly higher LCOS values (200–750 EUR/MWh). For both locations, the lowest LCOS values (180 EUR/MWh for Cagliari and 200 EUR/MWh for Berlin), obtained for very high curtailment levels (40%), are significantly above average electricity prices (108 EUR/MWh for Cagliari and 78 EUR/MWh for Berlin), suggesting that BESSs for curtailment mitigation are competitive in the day-ahead market only if their electricity is sold at a significantly higher price. This is particularly true for lower curtailment levels. Indeed, for a curtailment level of 10% reached in 5 years, the LCOS for a 0.5 h BESS capacity is approximately 255 EUR/MWh in Cagliari and 460 EUR/MWh in Berlin. The study further highlights that the curtailment scenario significantly affects the Levelized Cost of Storage, with the upper limit hourly profile being more conservative. Full article

The integration of battery energy storage systems (BESSs) within active distribution networks (ADNs) entails optimized day-ahead charge/discharge scheduling to achieve effective peak shaving.The primary objective is to reduce peak demand and mitigate power deviations caused by intermittent photovoltaic (PV) output. Quasi-static time-series (QSTS) co-simulations for determining optimal heuristic solutions at each time interval are computationally intensive, particularly for large-scale systems. To address this, a two-stage intelligent BESS scheduling approach implemented in a MATLAB–OpenDSS environment with parallel processing is proposed in this paper. In the first stage, a rule-based decision tree generates initial charge/discharge setpoints for community BESS units. These setpoints are refined in the second stage using an optimization algorithm aimed at minimizing community net load power deviations and reducing peak demand. By assigning each ADN community to a dedicated CPU core, the proposed approach utilizes parallel processing to significantly reduce the execution time. Performance evaluations on an IEEE 8500-node test feeder demonstrate that the approach enhances peak shaving while reducing QSTS co-simulation execution time, utility peak demand, distribution network losses, and point of interconnection (POI) nodal voltage deviations. In addition, the use of smart inverter functions improves BESS operations by mitigating voltage violations and active power curtailment, thereby increasing the amount of energy shaved during peak demand periods. Full article

Recent advancements in photovoltaic (PV) and battery technologies, combined with improvements in power electronic converters, have accelerated the adoption of rooftop PV systems and electric vehicles (EVs) in distribution networks, while these technologies offer economic and environmental benefits and support the transition to sustainable energy systems, they also introduce operational challenges, including voltage fluctuations, increased system losses, and voltage regulation issues under high penetration levels. Traditional Voltage and Var Control (VVC) strategies, which rely on substation on-load tap changers, voltage regulators, and shunt capacitors, are insufficient to fully manage these challenges. This study proposes a novel Voltage, Var, and Watt Control (VVWC) framework that coordinates the operation of PV and EV resources, conventional devices, and demand responsive loads. A mixed-integer nonlinear multi-objective optimization model is developed, applying a Chebyshev goal programming approach to balance objectives that include minimizing PV curtailment, reducing system losses, flattening voltage profile, and minimizing demand not met. Unserved demand has, in particular, been modeled while incorporating the concepts of distributional and recognition energy justice. The proposed method is validated using a modified version of the IEEE 123-bus test distribution system. The results indicate that the proposed framework allows for high levels of PV and EV integration in the grid, while ensuring that EV demand is met and PV curtailment is negligible. This demonstrates an equitable access to energy, while maximizing renewable energy usage. Full article

The higher integration of photovoltaic (PV) units is an inevitable component of smart city development. Thanks to smart meter devices that can record the exchange of power between the grid and customers, it is expected that homeowners and businesses will tend to install PV arrays on their rooftops and parking lots to benefit from selling power back to the grid. However, the overvoltage issue resulting from high PV penetration is a major challenge that necessitates the active power curtailment of PV units to ensure power grid stability. Fairness-oriented methods aim to minimize the active power of PV units as much as possible, adopting a fairer approach, and then address the PV owner’s satisfaction with fair profit and loss. Maintaining voltage within a limited standard range under very low load conditions while prioritizing PV inverters’ participation in reactive power contribution and attempting to ensure fairer curtailment of active power presents challenges to existing control design approaches. This paper presents twelve new volt–watt curve design methods to achieve these goals and address the challenges. The methods yield polynomial curves, piecewise linear curves, and single linear curves. A unique voltage sensitivity value for each PV inverter is used to determine the control region area and the slope of the curve at the starting point in certain instances. The effectiveness of the proposed methods is discussed by evaluating their capabilities on the 37-bus IEEE system. Full article

Electricity market reforms and decreasing technology costs have propelled residential solar PV growth leading distribution network operators to face operational challenges including reverse power flows and voltage regulation during peak solar generation. Traditional mono-facial south-facing PV systems concentrate production at midday when demand may be low, leading to high curtailment, especially for downstream households. This study proposes vertically installed east–west-facing bifacial PV systems (BiE and BiW), characterized by two energy peaks (morning and evening), which are better aligned with residential demand and alleviate grid constraints. Using load flow simulations, the performance of vertical bifacial configurations was compared against mono-facial systems across PV capacities from 1 to 20 kW. Fairness in curtailment was evaluated at 10 kW using Jain’s fairness index, the Gini index, and the Curtailment index. Simulation results show that BiE and BiW installations, especially at higher capacities, not only generate more energy but also are better at managing curtailment. At 10 kW, BiE and BiW increased bid energies by 815 kWh and 787 kWh, and reduced curtailed energy by 1566 kWh and 1499 kWh, respectively. These findings highlight the potential of bifacial PV installations in mitigating curtailment and improving fairness in energy distribution, supporting the demand for residential PV systems. Full article

Microgrids are considered a practical solution to revolutionize power systems due to their ability to island and sustain the penetration of renewables. Most existing studies have focused on the optimal management of microgrids with a fixed configuration. This restricts the application of developed algorithms to other configurations without major modifications. The objective of this study is to design a rule-based modular energy management system (EMS) for microgrids that can dynamically adapt to the microgrid configuration. To realize this framework, first, each component is modeled as a separate entity with its constraints and bounds for variables. A wide range of components such as battery energy storage systems (BESSs), electric vehicles (EVs), solar photovoltaic (PV), microturbines (MTs), and different priority loads are modeled as modules. Then, a rule-based system is designed to analyze the impact of the presence/absence of one module on the others and update constraints. For example, load shedding and PV curtailment can be permitted if the grid module is not included. The constraints of microgrid components present in any given configuration are communicated to the EMS, and it optimizes the operation of the available components. The configuration of microgrids could be as simple as flexible loads operating in grid-connected mode or as complex as a hybrid microgrid with AC and DC buses with a diverse range of equipment on each side. To facilitate the realization of diverse configurations, a hybrid AC/DC microgrid is considered where the utility grid and interlinking converter (ILC) are also modeled as separate modules. The proposed method is used to test performance in both grid-connected and islanded modes by simulating four typical configurations in each case. Simulation results have shown that the proposed rule-based modular method can optimize the operation of a wide range of microgrid configurations. Full article

UK Government projections anticipate increasing electricity use, provided by variable renewables (i.e., wind and solar PV). A side effect of increasing the proportion of variable renewable generation is increased support costs, including curtailment, energy storage, and (most significantly) the cost of supplying electricity for periods of high demand when variable renewable generation is low. As the proportion of variable renewable capacity increases, demand for supporting capacity increases but the capacity factor of the support generation decreases, raising the support costs. Using nuclear power for dedicated baseload supply makes the situation worse. This paper explores in the UK context an original low-cost solution using nuclear cogeneration with hydrogen production as the main application. Electricity is diverted at low cost to the grid at times of high demand when renewables are not available. This ensures nuclear maintains a high capacity factor. When higher temperature advanced systems become available, using thermal energy storage will increase the nuclear electrical capacity. This “Flexible Nuclear” scenario substantially reduces support costs for accommodating variable renewables, saving GBP 14 bn/yr and leading to an 80% reduction in CO₂ equivalent emissions, compared to a recent UK Government scenario utilising a large capacity of hydrogen and unabated gas generation at very low capacity factors. Full article

This work analyses load profiles for East African microgrids, and then investigates the integration of electric two-wheelers and portable storage into a solar PV with battery microgrid in Uganda, East Africa. By introducing e-mobility and portable storage, demand side management strategic load growth can thus be achieved and electricity access can be expanded. Battery degradation is also considered. The results showed a 98.5% reduction in PV energy curtailment and a 57% reduction in the levelized cost of energy (LCOE) from 0.808 USD/kWh to 0.350 USD/kWh when the electric two-wheeler and portable storage loads were introduced. Such reductions are important enablers of financial viability and sustainability of microgrids. It is possible to avoid emissions of up to 73.27 tons of CO₂/year with the proposed e-bikes, and an average of 160 customers could be served annually as off-microgrid consumers without requiring an investment in additional distribution infrastructure. Annual revenue could be increased by 135% by incorporating the additional loads. Sensitivity analyses were conducted by varying component costs, the battery lifetime, the interest rate, and the priority weighting of the additional loads. The battery costs were found to be a major contributor to lifecycle costs (LCC) and also have a big impact on the LCOE. The interest rate significantly affects the LCC as well. Full article

In order to address the challenges associated with optimizing multi-timescale operations and allocating ultra-short-term energy storage for HWP integration, this study takes into account both the economic and reliability aspects of the HWP integration base. It proposes a model for optimizing operations and allocating energy storage capacity, achieving optimization across long-term, short-term, and ultra-short-term operations for an MECB. Initially, operation optimization is implemented for an entire group of terraced hydropower plants by regulating them with annual regulating capabilities on a long-term timescale. The objectives are to maximize the daily average minimum output and annual power generation. Subsequently, short-term operation optimization focuses on maximizing HWP power feed-in, minimizing new energy power curtailment, and reducing residual load standard deviation while ensuring the guaranteed output optimization results for the long term. Finally, to mitigate ultra-short-term fluctuations in new energy, a HESS with specified capacity and power is configured with the goal of minimizing comprehensive costs. Additionally, to address the challenge of smoothing negative fluctuations, which is hindered by charging and discharging efficiency limitations, a variable baseline is introduced, deviating from the conventional 0 MW baseline. A simulation study based on data from the hydro–wind–PV hybrid project in the Beipanjiang River Basin, China, demonstrates the following: (1) after long-term system optimization, the total power generation capacity of the system increases by 9.68%, while the peak-to-valley difference in output is significantly reduced; (2) short-term system optimization significantly reduces both the average variance in residual loads and the amount of power curtailed over five representative days; (3) the system incorporates 398.62 MWh of lithium-ion battery storage with a power of 412.47 MW and 51.09 MWh of supercapacitor storage with a power of 223.32 MW, which, together, completely smooth out the ultra-short-term fluctuations in new energy output. Full article

With the proposal of the dual carbon target, the distributed photovoltaic (PV) industry has rapidly developed in recent years. However, the randomness and volatility of photovoltaic energy can be transmitted to the main grid through distribution network substations, posing challenges to the stable operation of the power system. Therefore, this paper considers tapping into the regulation potential of feeder loads on the distribution network side, as well as distributed energy storage and distributed PV resources, to enhance the grid’s control methods. A power fluctuation smoothing control strategy for substations in distribution networks, accounting for multiple types of regulation resources, is proposed. In the day-ahead stage, traditional voltage regulation devices such as the OLTC (on-load tap changer) and CB (circuit breaker) are pre-dispatched based on source–load forecasts, optimizing the fluctuation range of substation power and the number of device operations. This provides optimal substation power values for day-to-day optimization. During the intraday phase, fast regulation devices such as PV (photovoltaic), SVC (static var compensator), and energy storage systems are coordinated, and an optimization model is established with the goal of reducing power curtailment while closely tracking substation trends. This model quickly calculates the active power regulation and device operations of various adjustable resources, improving the economic efficiency of the distribution network system while achieving power fluctuation smoothing at the substation level. Finally, the feasibility and effectiveness of the power fluctuation smoothing control model are verified through simulations on an improved standard distribution system. Full article

Electrochemical energy storage (EES) plays a crucial role in reducing the curtailed power from wind and solar PV power (WSP) generation and enhancing the decarbonization effects of power systems. However, research on quantifying the carbon emission reduction effects of EES methods in the engineering field is still insufficient, which constrains decision-makers from making intuitive assessments of the decarbonization effects of energy storage. Therefore, drawing on the principles of the clean development mechanism (CDM), this paper proposes a method for quantifying the carbon emission reductions of a standalone EES station. Firstly, based on the design principles of building marginal emission factor in the CDM, a method for calculating the BM weight of WSP is proposed. Secondly, three quantification methods for the carbon emission reductions of EES are presented based on the complexity of the calculations. Lastly, to analyze the impacts of different operational conditions and calculation methods on the carbon emission reduction of energy storage systems, a dispatch model is constructed for various operational scenarios. The results of the case study indicate that different calculation methods yield varying results in terms of the carbon emission reductions of energy storage systems, with the sharply value method yielding the smallest reduction and the output curve method yielding the largest reduction. Additionally, when considering the losses in the state of charge (SOC) of an energy storage system and reducing the overall output fluctuations of WSP-EES, the carbon emission reduction potential of the energy storage will decrease. This study establishes a theoretical basis for quantifying the carbon emission reductions of standalone electrochemical energy storage systems, aiding decision-makers in gaining a deeper understanding of the role of electrochemical energy storage in carbon reduction and operational value. Full article

Integrating photovoltaic power stations into large-capacity hydropower stations is an efficient and promising method for regulating large-scale photovoltaic power generation. However, constrained by the time step length, traditional long-term scheduling of hydro-PV hybrid systems does not adequately consider short-term operational performance indicators, resulting in sub-optimal scheduling plans that fail to coordinate the consumption of photovoltaic power and the utilization of water resources in the basin. To address this, this study established a long-term optimal scheduling model for hydro-PV hybrid systems. This model overcomes the limitation of the time step length in long-term scheduling by incorporating long-term power generation goals and short-term operation performance targets into the long-term optimal scheduling process based on scheduling auxiliary functions. In case studies, the optimised model balanced the long-term power-generation goals and short-term operational performance targets by redistributing energy across different periods. Compared to optimization models that did not consider short-term operation performance, in a typical normal year, the model effectively reduced the electricity curtailment volume (28.54%) and power shortage volume (10.91%) of the hybrid system while increasing on-grid electricity (0.03%). Similar improvements were observed in wet and dry years. These findings provide decision support for hydropower scheduling in the context of large-scale photovoltaic power integration. Full article

Configuring energy storage systems (ESSs) in distribution networks is an effective way to alleviate issues induced by intermittent distributed generation such as transformer overloading and line congestion. However, flexibility has not been fully taken into account when placing ESSs. This paper proposes a novel ESS placement method for flexible interconnected distribution networks considering flexibility constraints. An ESS siting and sizing model is formulated aiming to minimize the life-cycle cost of ESSs along with the annual network loss cost, electricity purchasing cost from the upper-level power grid, photovoltaic (PV) curtailment cost, and ESS scheduling cost while fulfilling various security constraints. Flexible ramp-up/-down constraints of the system are added to improve the ability to adapt to random changes in both power supply and demand sides, while a fluctuation rate of net load constraints is also added for each bus to reduce the net load fluctuation. The nonconvex model is then converted into a second-order cone programming formulation, which can be solved in an efficient manner. The proposed method is evaluated on a modified 33-bus flexible distribution network. The simulation results show that better flexibility can be achieved with slightly increased ESS investment costs. However, a large ESS capacity is needed to reduce the net load fluctuation to low levels, especially when the PV capacity is large. Full article

The volatility and uncertainty associated with the high proportion of wind and PV output in the new power system significantly impact the power and energy balance, making it challenging to accurately assess the risks related to renewable energy abandonment and supply guarantee. Therefore, a probabilistic power and energy balance risk analysis method based on distributed robust optimization is proposed. Firstly, the affine factor and the flexible ramp reserve capacity of thermal power are combined to establish a probabilistic index, which serves to characterize the risk associated with the power and energy balance. Drawing upon the principles of the conditional value at risk theory, the risk indexes of the load shedding power and curtailment power under a certain confidence probability are proposed. Secondly, the probability distribution fuzzy sets of uncertain variables are constructed using the distributionally robust method to measure the Wasserstein distance between different probability distributions. Finally, aiming at minimizing the operation cost of thermal power, the risk cost of power abandonment, and the risk cost of load shedding, a distributed robust optimal scheduling model based on a flexible ramp reserve of thermal power is established. Full article