Uncertainty Of Renewable Energy Generation Research Articles

Reactive Power Optimization in Distribution Networks of New Power Systems Based on Multi-Objective Particle Swarm Optimization

The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions.

Bidding strategies for multi-microgrid markets taking into account risk indicators

A large proportion of new energy generation is integrated into the power grid, making it difficult for the power grid system to maintain reliable, stable, and efficient operation. To address these challenges, this article proposes a multiple microgrid hierarchical optimization structure based on energy routers as the core equipment for energy regulation within microgrids. Considering the uncertainty of renewable energy generation within microgrids, a two-layer energy management bidding strategy based on risk indicators is further proposed. In the process of trading, with the goal of maximizing a comprehensive economy, the energy trading model of the distribution network center and energy routers is divided into two sub-objectives for solving. In the first stage, based on the interests and energy supply and demand relationships of each microgrid, a risk assessment model considering wind and solar uncertainty is established to determine the risk preferences and expected returns of each microgrid. In the second stage, the original problem is decomposed into two sub-problems: the minimum cost sub-problem and the maximum transaction volume sub-problem. An asymmetric bargaining mechanism is adopted to determine the production and sales payment of the microgrid containing energy routers based on the contribution values of energy routers in each microgrid. Finally, the rationality and effectiveness of energy routers as an intelligent decision-maker in energy optimization are verified in a three-node system.

Reinforcement learning based bilevel real-time pricing strategy for a smart grid with distributed energy resources

The integration of flexible loads, distributed energy resources, and other technologies is becoming common in advance power and energy systems. However, the integration also presents significant challenges due to the increasing complexity and uncertainty. To effectively manage these resources, an adaptive pricing mechanism is needed that can account for their variable availability. Based on this, we propose a new bilevel real-time pricing model that considers different distributed energy resources, uncertainty of renewable energy generation, carbon trading mechanisms, and grid fluctuations. Specifically, the upper-level optimization problem aims to maximize the total profit of the supplier that applies Q-learning algorithm. The lower-level optimization problem addresses the need for each user to make optimal power consumption decisions by constructing an individual Markov Decision Process framework for each user. The bilevel model achieves an effective balance of interests between the supplier and users by simultaneously considering both the upper-level and lower-level optimization problems. Additionally, our model can be efficiently solved using the distributed algorithm without the need to acquire transition probabilities. Simulation results show that the method is highly effective in balancing power supply and demand between the supplier and users, reducing carbon emissions, and mitigating power fluctuations.

Probabilistic Forecasting-Based Reserve Determination Considering Multi-Temporal Uncertainty of Renewable Energy Generation

Operating reserve, among the most important ancillary services, is a powerful prescription for mitigating increasing uncertainty of renewable generation. Traditional reserve determination neglects uncertainty of renewable generation variations within the dispatch interval, which cannot gurantee sufficient reserve deliverability in real-time operation. This paper proposes a probabilistic forecasting-based reserve determination method to efficiently deal with multi-temporal uncertainty of renewable energy in power systems. A novel probabilistic forecasting approach is proposed by integrating Dirichlet process Gaussian mixture model into bootstrap-based extreme learning machine. A unified uncertainty model is constructed for depicting both interval-averaged uncertainty and intra-interval uncertainty by combing the probabilistic forecasts and linearized Itô-process model. In current business practices of many independent system operators, the coordinated determination of two well-designed reserve products, including regulating reserve and ramp capability reserve, is formulated in a two-stage robust optimization framework. A Bernstein polynomial-based model reformulation approach is then employed to handle the existing heterogeneity in the sub-models of each stage. Consequently, the integrated reserve determination is embedded in a generic two-stage robust optimization problem, which can be efficiently solved by the adopted modified column-and-constraint generation method. Finally, numerical simulations are implemented to validate the effectiveness and profitability of the proposed approach.

Rechargeable Lithium-ion and Aqueous Zn-based Battery

In the context of frequent extreme events around the world, economic and social development needs to utilize renewable energy. However, most renewable energy sources have the disadvantage of intermittency. It requires energy storage technology to balance supply and demand. Secondary battery energy storage technology can improve energy utilization efficiency and reduce the uncertainty of renewable energy generation. This paper introduces two kinds of energy storage technology, lithium-ion rechargeable battery and aqueous Zn-based rechargeable battery. Li-ion battery is prone to safety accidents such as combustion and explosion, and it will produce toxic substances. The aqueous Zn-based battery has high safety, and the production and using process is environmentally friendly. Compared with Li-ion, aqueous Zn-based has a lower area capacity, which is not conducive to large-scale commercial production applications. Due to the restriction of lithium resources, the production cost of lithium batteries is gradually increasing. In the future, in the development of energy storage technology, both technologies have certain durability and unique advantages, but there are also obvious limiting factors that need to be studied and improved.

Cross-regional green certificate transaction strategies based on a double-layer game model

With the implementation of the renewable energy quota system and the development of the green certificate market, the diversification of cross-region trading subjects has brought about the problem of complicating the competition and cooperative relationship between multiple subjects. This problem makes it difficult to balance the conflict of interests of multiple participating subjects in green certificate transactions, and reduces the enthusiasm for trading. To this end, this paper proposes a multi-participant decision model of cross-regional green certificate trading behavior that takes into account the uncertainty of renewable energy generation. In the model, in order to explore the potential full set of strategies of the participating subjects, the cooperative game theory is utilized to analyze the complex interaction between multiple participating subjects in the cross-regional transaction, and the cooperative game model based on green certificates is established. As the participating subjects' information about other participants and their knowledge of the game environment are limited, the decision-making behavior is uncertain. In order to more reasonably portray the subject's game behavior in the actual decision-making scenario and to solve the defect that the traditional evolutionary game model can not reflect the influence of uncertain factors, we use the stochastic evolutionary game theory to dynamically deduce the cooperative strategies of the participating subjects under the limited rationality, and at the same time, introduce the strategy influencing factors to explore the dependence relationship between strategies. In addition, in order to cope with the impact of power generation uncertainty on cross-regional trading and to solve the defects of distributionally robust fuzzy set construction which is subjective and only for the analysis of uncertainty in a single time section, it is proposed to use Bayesian multivariate nonlinear model and mixed-effects model to construct probability distribution fuzzy sets of wind and light power generation uncertainty on the basis of distributionally robust theory. Finally, based on the arithmetic example, the cooperative strategy of multi-participants in cross-regional transactions under finite rationality is deduced, and the impact of uncertainty factors on the cooperative strategy is analyzed.

A Stackelberg game-based dynamic pricing and robust optimization strategy for microgrid operations

Energy management algorithms of microgrids have attracted growing attention for the efficient operation and coordination of active end-users. In developing these algorithms, leader–follower game approaches could provide effective strategies when the behavioral and operational characteristics of active end-users are integrated into the methodology. However, since these approaches require the exact implementation of the strategy determined by the leader, it is of great importance to deal with the uncertainty of renewable energy generation, charging/discharging energy of storage systems, and load consumption. Thus, possible negative effects of these uncertainties on the outcome can be minimized with an appropriate billing procedure. In this paper, a Stackelberg game approach is proposed for the energy sharing management of a microgrid including prosumers and plug-in electric vehicle (PEV) charging stations (CSs), and a proper billing scheme is designed to increase the robustness of the approach on real-time applications. In the hereby game-theoretic approach, the energy demands of PEV CSs are determined as variable demands considering the operational characteristics of CSs. The energy planning of CSs is based on determining the number of PEVs that each station can accept over the vehicle charging levels for a specified time interval. Also, by the necessary modifications identified for the presented approach, the microgrid can operate independently from the utility grid and satisfy the requirements of islanded mode for the time slots in which the microgrid exchanges energy with the grid based on the end-game results. The energy management strategy proposed in this study presents a comprehensive solution capable of optimizing energy sharing in microgrids while taking into account end-users’ operational characteristics and network requirements. Furthermore, the designed billing scheme ensures that the accuracy of the proposed game approach remains unaffected by the uncertainties, thus preserving the microgrid’s profit.

A novel approach to hybrid dynamic environmental-economic dispatch of multi-energy complementary virtual power plant considering renewable energy generation uncertainty and demand response

Renewable energy generation significantly reduces harmful emissions and promotes sustainable development. Still, its volatility leads to extensive grid connections, affecting electricity system security. Diversifying forms of energy consumption further expands the peak valley difference in electricity demand. This study aims to investigate the hybrid dynamic environmental-economic dispatch problem of multi-energy complementary virtual power plant, considering the renewable energy generation uncertainty and demand response to promote renewable energy integration and mitigate the electricity system supply-demand mismatch. It proposes a novel approach based on the sine cosine and multi-objective particle swarm optimization algorithm to reduce the economic and environmental costs of the multi-energy complementary virtual power plant. First, a hybrid dynamic environmental-economic dispatch model of the multi-energy complementary virtual power plant is established, considering climbing power, equality, and inequality constraints. Second, targeting the multi-objective, nonlinear, and high-dimension characteristics of the hybrid dynamic environmental-economic dispatch model of the multi-energy complementary virtual power plant, a sine cosine and multi-objective particle swarm optimization algorithm is proposed to optimize the particle position update method. Finally, simulation cases are constructed based on the development trend of the virtual power plant, setting various dispatching situations to validate the robustness of the proposed approach and determining a compromise solution by membership functions. The simulation results show that the lowest economic and environmental costs obtained by the sine cosine and multi-objective particle swarm optimization algorithm are at least 11.37 % and 2.79 % lower than those obtained by the NSGA-II algorithm and multi-objective particle swarm optimization algorithm when considering the renewable energy generation uncertainty and demand response. Therefore, the work contributes to decreasing the economic and environmental costs of multi-energy complementary virtual power plant and better enhancing the consumption ratio of renewable energy.

Optimal Allocation of Energy Storage Capacity in Microgrids Considering the Uncertainty of Renewable Energy Generation

The high dimensionality and uncertainty of renewable energy generation restrict the ability of the microgrid to consume renewable energy. Therefore, it is necessary to fully consider the renewable energy generation of each day and time period in a long dispatching period during the deployment of energy storage in the microgrid. To this end, a typical multi-day scenario set is used as the simulation operation scenario, and an optimal allocation method of microgrid energy storage capacity considering the uncertainty of renewable energy generation is designed. Firstly, the historical scenarios are clustered into K types of daily state types using the K-means algorithm, and the corresponding probability distribution is obtained. Secondly, the Latin hypercube sampling method is used to obtain the state type of each day in a multi-day scenario set. Then, the daily scenario generation method based on conditional generative adversarial networks is used to generate a multi-day scenario set, combining the day state type as a condition, and then the typical scenario set is obtained using scenario reduction. Furthermore, a double-layer optimization allocation model for the energy storage capacity of microgrids is constructed, in which the upper layer optimizes the energy storage allocation capacity and the lower layer optimizes the operation plans of microgrids in each typical scenario. Finally, the proposed model is solved using the PSO algorithm nested with the CPLEX solver. In the microgrid example, the proposed method reduces the expected annual total cost by 19.66% compared with the stochastic optimal allocation method that assumes the scenic power obeys a specific distribution, proving that it can better cope with the uncertainty of renewable energy generation. At the same time, the expected annual total cost is reduced by 6.99% compared with the optimal allocation method that generates typical daily scenarios based on generative adversarial networks, which proves that it can better cope with the high dimensionality of renewable energy generation.

A local integrated electricity-heat market design among multi Smart Energy Hubs with renewable energy generation uncertainty

A local integrated electricity-heat market design among multi Smart Energy Hubs with renewable energy generation uncertainty

Robust optimization and pricing of Peer-to-Peer energy trading considering battery storage

Peer-to-peer energy trading is one of the greatest systems that helps to increase the use of renewable energy, such as photovoltaics and wind turbines, in households due to the escalating environmental issues. This study designs a peer-to-peer energy trading system under the uncertainty of renewable energy generation and peers’ demands. This study uses a cardinality-constrained robust optimization approach to tackle uncertainties, and it is solved to minimize the overall costs of energy trading, battery depreciation, and load shedding by the Alternating Direction Method of Multipliers method. This model considers peers who are equipped with distributed generation and batteries. Moreover, this local grid has a central battery storage in order to reduce load shedding and dependence on the power grid. Furthermore, the Nash bargaining theory is modeled to calculate energy prices that are traded by peers in order to boost the benefit of all peers and encourage them to use renewable energy as well as participate in peer-to-peer energy trading. Finally, based on data from Iran, the mathematical model is solved to show the effectiveness of using peer-to-peer energy trading that not only can save significant power but also make substantial profits for peers.

Developing a strategy based on weighted mean of vectors (INFO) optimizer for optimal power flow considering uncertainty of renewable energy generation.

In recent years, more efforts have been exerted to increase the level of renewable energy sources (RESs) in the energy mix in many countries to mitigate the dangerous effects of greenhouse gases emissions. However, because of their stochastic nature, most RESs pose some operational and planning challenges to power systems. One of these challenges is the complexity of solving the optimal power flow (OPF) problem in existing RESs. This study proposes an OPF model that has three different sources of renewable energy: wind, solar, and combined solar and small-hydro sources in addition to the conventional thermal power. Three probability density functions (PDF), namely lognormal, Weibull, and Gumbel, are employed to determine available solar, wind, and small-hydro output powers, respectively. Many meta-heuristic optimization algorithms have been applied for solving OPF problem in the presence of RESs. In this work, a new meta-heuristic algorithm, weighted mean of vectors (INFO), is employed for solving the OPF problem in two adjusted standard IEEE power systems (30 and 57 buses). It is simulated by MATLAB software in different theoretical and practical cases to test its validity in solving the OPF problem of the adjusted power systems. The results of the applied simulation cases in this work show that INFO has better performance results in minimizing total generation cost and reducing convergence time among other algorithms.

Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response

A community integrated energy system (CIES) is an important carrier of the energy internet and smart city in geographical and functional terms. Its emergence provides a new solution to the problems of energy utilization and environmental pollution. To coordinate the integrated demand response and uncertainty of renewable energy generation (RGs), a data-driven two-stage distributionally robust optimization (DRO) model is constructed. A comprehensive norm consisting of the 1-norm and ∞-norm is used as the uncertainty probability distribution information set, thereby avoiding complex probability density information. To address multiple uncertainties of RGs, a generative adversarial network based on the Wasserstein distance with gradient penalty is proposed to generate RG scenarios, which has wide applicability. To further tap the potential of the demand response, we take into account the ambiguity of human thermal comfort and the thermal inertia of buildings. Thus, an integrated demand response mechanism is developed that effectively promotes the consumption of renewable energy. The proposed method is simulated in an actual CIES in North China. In comparison with traditional stochastic programming and robust optimization, it is verified that the proposed DRO model properly balances the relationship between economical operation and robustness while exhibiting stronger adaptability. Furthermore, our approach outperforms other commonly used DRO methods with better operational economy, lower renewable power curtailment rate, and higher computational efficiency.

Multi-objective optimal scheduling of charging stations based on deep reinforcement learning

With the green-oriented transition of energy, electric vehicles (EVs) are being developed rapidly to replace fuel vehicles. In the face of large-scale EV access to the grid, real-time and effective charging management has become a key problem. Considering the charging characteristics of different EVs, we propose a real-time scheduling framework for charging stations with an electric vehicle aggregator (EVA) as the decision-making body. However, with multiple optimization objectives, it is challenging to formulate a real-time strategy to ensure each participant’s interests. Moreover, the uncertainty of renewable energy generation and user demand makes it difficult to establish the optimization model. In this paper, we model charging scheduling as a Markov decision process (MDP) based on deep reinforcement learning (DRL) to avoid the afore-mentioned problems. With a continuous action space, the MDP model is solved by the twin delayed deep deterministic policy gradient algorithm (TD3). While ensuring the maximum benefit of the EVA, we also ensure minimal fluctuation in the microgrid exchange power. To verify the effectiveness of the proposed method, we set up two comparative experiments, using the disorder charging method and deep deterministic policy gradient (DDPG) method, respectively. The results show that the strategy obtained by TD3 is optimal, which can reduce power purchase cost by 10.9% and reduce power fluctuations by 69.4%.

Improved utilization of hybrid energy for low-income houses based on energy consumption pattern

<abstract> <p>The adoption of solar photovoltaic and small wind turbine hybrid energy systems in residential applications has picked up promising development around the globe. However, the uncertainty of renewable energy generation associated with the reliance on climate conditions is one of the factors which affect the reliability of the system. Therefore, there is a need to develop an energy management scheme for improving the reliability of the system. One of the drawbacks of hybrid renewable energy systems is the high investment cost, particularly looking at low-income family units. This present paper, an extension of the preceding work, focused on the development of an energy utilization scheme of a hybrid energy system particularly for low-income houses based on energy consumption patterns. The utilization scheme is developed using computational methods in a MATLAB environment. Energy storage systems considered in this work are electrochemical batteries and small-scale flywheel energy storage (kinetic energy storage). Utilizing hybrid energy based on consumption patterns has lowered the capacity of the system's components, resulting in a 0.00 investment cost. The flywheel energy storage is prioritized to supply high-wattage loads while the battery is prioritized to supply average loads, resulting in a 33.9% improvement in battery health. This hybrid system contains a high proportion of renewable energy and reduces annual electricity costs by 96.7%. The simulated results on MATLAB software showed an improvement in terms of energy utilization of a hybrid power system. The cost of utilizing energy is reduced by effectively utilizing more renewable energy sources, with a resultant reduction in electricity bills.</p> </abstract>

Robust Survivability-Oriented Scheduling of Separable Mobile Energy Storage and Demand Response for Isolated Distribution Systems

Extreme circumstances in which a local distribution system is electrically isolated from the main power supply may not always be avoidable. Efforts must be made to keep the lights on for such an isolated distribution system (IDS) until reconnection to the main power source. In this paper, we propose a strategy to enhance IDS survivability utilizing the coordination of two flexible approaches, namely, separable energy storage systems (SMESSs), which construct non-wires links for energy transmission between the IDS and the external live power sources, and demand response (DR), which adjusts the internal electrical demand of the IDS to provide effective operating stress alleviation. Considering the uncertainty of renewable energy generation and loads, a two-stage robust optimization (RO) model involving the joint scheduling of these two approaches is constructed. The objective is to minimize the fuel consumption and the decreased and nonserved demand under the worst-case scenario to endow the IDS with extended survivability. Finally, test is conducted and the results demonstrate the effectiveness of the proposed method in enhancing the survivability of IDS.

Optimal Scheduling Research for Day Ahead Market Transaction of Virtual Power Plant Considering Uncertainty and CVaR

Considering the uncertainty of renewable energy generation and the market transaction risk in the day ahead market of virtual power plants, an optimal scheduling model of virtual power plant in the day ahead market was presented. Firstly, this study modeled and analyzed the uncertainty of wind and photovoltaic power generation fluctuations in the virtual power plant. In addition, based on the conditional value at risk theory and the day ahead market clearing rules, the optimal scheduling model of virtual power plants in the day ahead market was established aiming at minimizing the implementation deviation and maximizing the operating income. Finally, the transaction data of a typical region was selected for case study to verify the effectiveness and the feasibility of the optimal scheduling strategy of virtual power plant in day ahead market transaction.

Research on a cross-regional robust trading strategy based on multiple market mechanisms

With the increase in the variety of market players in cross-regional trading, the interaction of interests among multiple participants is complicated, resulting in unbalanced interests in the cross-regional trading market. Therefore, a robust trading model with joint multi-market mechanisms is proposed in this paper to cope with the intricate interactions among market players. The proposed model builds a joint trading market framework of carbon emission rights and green certificates to incentivize more market players to participate in cross-region trading. To better balance the interests among the market players, a multi-market player alliance based on the cooperative game theory is established, and a profit distribution method based on the contribution degree of system power balance is proposed. To ensure the balance of energy supply and demand for cross-regional trading, the distributed robust optimization method based on the Wasserstein metric is proposed considering the uncertainty of renewable energy generation. Finally, the feasibility of the proposed dispatching model is demonstrated with the modified IEEE 39 bus system and the Hami power grid as an example. The results show that the proposed model can obtain a conservative and economical operating scheme with fast operation time, and the joint trading mechanism can further save the operating cost.

A Risk-Averse Energy Sharing Market Game for Renewable Energy Microgrid Aggregators

Energy sharing has the potential to decrease operation costs by exploiting the complementarity of different entities. The further increase of microgrids’ generation capacity in the distribution network could trigger the emergence of MW-level microgrids aggregator (MGA) to participate in the regional energy sharing market (ESM) and sell excess energy. However, the uncertainty of renewable energy generation will cause the failure of energy sharing and an economic loss for the energy sharing market. Therefore, a novel two-sided market with the clearing price and risk management is proposed in this paper. In order to optimize the energy sharing strategy of MGAs, a network-constrained risk-averse two-stage stochastic game model based on DC optimal power flow (OPF) is established. The objective of the MGA is to maximize the revenue in the ESM while the subtransmission network operator (SNO) aims to achieve regional energy self-sufficiency. The sample average approximation (SAA) method is employed to approximate the stochastic Cournot-Nash equilibrium. A distributed market clearing algorithm based on the alternating direction method of multipliers (ADMM) and best response seeking is developed to solve the optimal bidding problem of MGAs and the optimal dispatch problem of the SNO. Furthermore, the existence of the SAA Nash equilibrium is also investigated. Numerical simulations prove that the proposed game model can effectively control the overbidding risk and promote regional energy self-sufficiency.

Cloud data center participation in smart demand response programs for energy cost minimisation

Demand Response Programs (DRPs) in Smart Grid (SG) are designed to encourage consumers to shift their loads to regions and hours with less load stress by alternating the price of electricity. On the other hand, with the emergence of cloud computing, the demand for cloud data centres increases resulting in high energy costs. In this paper, we address the problem of cloud data centre demand response in an SG area in presence of utility companies which compete to attract more customers. We formulate two optimization problems for cloud and data centres to minimise their costs by changing their energy demand. These problems are shown to be convex and can be readily solved by standard convex optimization techniques. We also propose an algorithm for cloud and data centres to participate in DRPs by concurrently performing regional and temporal workload management. The uncertainty of renewable energy generation in data centres is treated by training a multilayer perceptron to predict the generated energy. Numerical results show that the proposed algorithm outperforms other existing algorithms in terms of the energy cost. In addition, our algorithm flattens the energy demand profile of utility companies and balances the electric load across different locations.