Storage Systems In Distribution Networks Research Articles

Probabilistic optimal planning of multiple photovoltaics and battery energy storage systems in distribution networks: A boosted equilibrium optimizer with time-variant load models

In recent years, there has been a rapid increase in distributed generation (DG) technologies incorporated into distribution networks (DNs) to meet the challenge of load growth. However, the stochastic nature of renewable energy, such as photovoltaic (PV), makes the amount of energy produced uncertain. This uncertainty, along with changes in generation and load demand, can increase energy losses and voltage instability. To address this issue, energy storage systems can be integrated to decrease the effects of the intermittency associated with renewable technologies. This paper proposes a new variant of an equilibrium optimizer (EO) based on reinforced learning, named RLEO, for optimal incorporation of multiple battery energy storage (BES) units integrated synchronously with solar PVs into distribution systems while minimizing energy loss. The RLEO algorithm employs reinforced learning mechanisms to prevent premature convergence of the EO and improve its exploration and exploitation capabilities. The performance of the RLEO algorithm is assessed using standard CEC 2017 benchmark functions and compared with the original EO and other popular algorithms using various statistical criteria. The RLEO algorithm is also applied to determine the optimal size and position of multiple PV units in IEEE 69-bus and 118-bus DNs with single and multi-objective optimization problems. Using the developed algorithm, the optimal arrangement of three non-dispatchable PVs results in a slight increase in the percentage reduction of energy loss across various load profiles: 53.0035 % for commercial, 19.6372 % for industrial, and 28.1783 % for residential. In contrast, by employing the optimal configuration of three PV + BES units, the reduction in energy loss percentage experiences a remarkable surge to 68.3466 % for commercial, 68.0917 % for industrial, and 68.1779 % for residential load scenarios using the proposed algorithm. This clearly indicates that the proposed RLEO algorithm significantly surpasses recent optimization methods documented in the literature when it comes to addressing the challenge of optimal allocation for multiple DGs. Furthermore, its applicability extends to more intricate optimization problems.

Two‐stage self‐adaption security and low‐carbon dispatch strategy of energy storage systems in distribution networks with high proportion of photovoltaics

AbstractWith the goal of achieving carbon neutrality, active distribution networks (DNs) with a high proportion of photovoltaics (PVs) are facing challenges in maintaining voltage stability and low‐carbon operation. Energy storage systems (ESSs), which have the ability to store and transfer energy temporarily, can be used as effective measures to enhance the capacity of consuming PVs and reduce carbon emissions in DNs. However, existing low‐carbon dispatch strategies for multiple sources, storages and loads fail to consider voltage violations, while the temporal carbon emission intensity of the upper‐level power grid is also often overlooked, which is an important factor that affects the dispatch strategy. Therefore, a two‐stage self‐adaptive dispatch strategy of ESSs that considers the temporal characteristics of slack nodal carbon emission intensity to minimise carbon emissions while maintaining voltage stability in DNs with high access to PVs is proposed. First, the framework of the proposed two‐stage self‐adaptive dispatch strategy of ESSs is established by taking into account the effects of ESSs on adjusting voltages and reducing carbon emissions, respectively, with the two‐stage switch principle of two operation modes being determined. On this basis, an optimization dispatch model is established to improve voltages and carbon emissions, and the optimal day‐ahead dispatch strategy of ESSs can be obtained by solving the model using genetic algorithm. Case studies of the modified 10 kV IEEE 33‐node DN and IEEE 123‐node DN verify the feasibility and superiority of the proposed two‐stage self‐adaptive security and low‐carbon day‐ahead dispatch strategy for ESSs, showing that the voltage stabilisation and lower carbon emissions of DNs are both improved.

Resilience‐oriented planning and management of battery storage systems in distribution networks

AbstractThe occurrence of unforeseen incidents is one of the major causes of extensive blackouts and financial damages to electrical networks. The utilization of distributed energy resources (DERs) as a local energy supplier can potentially reduce the destructive effects of unforeseen events along with their benefits for the normal operation of the network. In this paper, a new framework is proposed for the optimal siting and sizing of solar photovoltaic distributed generations (PVDGs) and battery energy storage systems (BESSs) in the distribution network to increase resiliency against the earthquake event considering the advantages of these resources in both normal and event conditions. Furthermore, an optimal energy pattern for BESSs has been provided that, in addition to being used for the normal condition, also prepares them for transferring to the emergency state. The objective function is formulated as a mixed‐integer linear programming (MILP) problem aiming at minimizing both the normal and emergency costs. Eventually, the numerical results of implementing the proposed model on the IEEE‐33 bus system are presented to illustrate its effectiveness and accuracy. These results show by using PVDG installation, the resilience can be improved by 14% while total installation and operating costs are reduced by 1.7%. It is possible, however, to decrease the load curtailment up to 55.46% by involving BESSs along with PVDGs, despite an 8.57% increase in costs.

FEATURES OF ORGANIZATION AND USE OF ENERGY STORAGE SYSTEMS IN DISTRIBUTION NETWORKS

The article analyzes the features and formulates recommendations for the most effective use of the energy storage systems in electrical distribution systems and microgrids in the context of expanding the use of local renewable energy sources. Information about various energy storage technologies is summarized and recommendations are formulated regarding their best applying for various applications in the electric power industry. The tasks that can and should be solved by energy storage systems in distribution networks are considered, as a result of which the expediency of forming hybrid storage systems is substantiated. Based on the analysis of the bibliography and consideration of international experience, the main problems have been identified that require further research for a reasonable choice of the structure, parameters, locations and modes of operation of hybrid energy storage systems, taking into account the specifics of the structure and operation of domestic electric distribution systems with local energy sources. Under the implementation of hybrid energy storage systems, a fundamentally new problem of determining the optimal parameters of individual components of such systems arises, which was practically not covered in published works on this issue. Additionally, difficulty in solving this task is the need for its complex solution, since, firstly, the parameters of the individual components of the hybrid energy storage system are most likely interdependent, and secondly, it is necessary to take into account the involvement of the storage systems in the solution of a number of optimization problems inherent in the electrical distribution systems. In order to facilitate the use of energy storage devices, it is advisable to create a comprehensive standard that allows you to evaluate and compare the quality and performance of different technologies, helps energy storage users justify the type and parameters, as well as optimal placement for maximum benefit.

Optimal allocation of a wind turbine and battery energy storage systems in distribution networks based on the modified BES-optimizer

Recently, incorporating renewable energy resources (RERs) like wind turbines (WTs) in a distribution network is rapidly increased to meet the load growth. However, distribution networks have been facing many challenges to withstand the intermittent output power of RERs. Battery energy storage (BES) is used with RERs to smoothly inject the output power to the grid by RERs. Therefore, this paper proposes an effective strategy for optimal allocation of WT and BES in RDS to decrease the total system losses. In addition, a modified bald eagle search (BES-optimizer) is proposed to obtain the preferable allocations of WT and BES simultaneously in the radial distribution system (RDS) considering the probabilistic distribution of the WT and load demand. IEEE 69-bus RDS is utilized as a test system. Based on the obtained results, installing WTs with BES gives better results than installing WTs alone in the RDS. However, the proposed algorithm proved its efficiency to obtain the best global results compared with other well-known techniques.

Sizing and Sitting of Battery Energy Storage Systems in Distribution Networks with Transient Stability Consideration

In this study, the capacity and location of battery energy storage systems (BESSs) in a distribution network were evaluated to increase the stability and reliability of power systems by applying the proposed transient stability indicators. The search capability of particle swarm optimization (PSO) combined with Pareto optimality in MATLAB R2019b was used to solve multi-objective problems in steady-state conditions, including active power loss, voltage deviation, and total operating cost. The BESS capacity and location were then derived using the Manhattan distance method. Subsequently, the BESS control model was constructed using the commercial software DIgSILENT PowerFactory 2021 to evaluate generator power smoothing and short-term voltage stability index during the transient response and determine the final capacity and location of BESS in distribution networks. Finally, the accuracy of the proposed method was verified by considering the daily load and solar power generation curves using the IEEE 33-bus radial power distribution network.

Distributed control of battery energy storage systems in distribution networks for voltage regulation at transmission–distribution network interconnection points

This paper describes a control framework that enables distributed battery energy storage systems (BESS) connected to distribution networks (DNs) to track voltage setpoints requested by the transmission system operator (TSO) at specific interconnection points in an optimal and coordinated manner. The control design is based on an optimisation problem whose objective is to minimise the real-time voltage-tracking mismatch while satisfying local physical network constraints. A novel agent-based control scheme adopting an online convex optimisation (OCO) framework is developed and solved in a distributed fashion to guarantee the solution’s scalability and the service provision within the required time. The BESS agents under the proposed control framework automatically adapt to the time-varying network conditions so as to track the required voltage setpoints whilst fulfilling the technical operating requirements of the local network. The designed OCO-based framework addresses the existing conflict between the accuracy and optimality of the solution and the communication and computational efficiency. The convergence to the optimal solution is demonstrated. Several case studies are performed to corroborate the analytical results and assess the performance of the proposed approach.

Optimal sizing and operations of shared energy storage systems in distribution networks: A bi-level programming approach

Rather than using individually distributed energy storage frameworks, shared energy storage is being exploited because of its low cost and high efficiency. However, proper sizing and operations approaches are still required to take advantage of shared energy storage in distribution networks. This paper proposes a bi-level model to optimize the size and operations of shared energy storage in hybrid renewable-resource power generation systems. The upper-level model maximizes the benefits of sharing energy storage for the involved stakeholders (transmission and distribution system operators, shared energy storage operators and the various power plant owners) and the lower-level model minimizes the hybrid system operating costs. The benefits of this system were found to be: (1) reductions in wind and solar power curtailment and coal-fired generation costs; (2) peak shaving; (3) frequency regulation; and (4) a deferral of upgrades to power transmission and distribution facilities. To fully realize the long-term planning and short-term operational interactions of shared energy storage, a bi-level nested genetic algorithm was designed to solve the proposed model. By continuously updating the solutions, a set of optimal solutions were generated. To validate the approach, numerical tests were conducted, with the results showing that by properly sizing and operating the shared energy storage in distribution networks, the wind curtailment rate was reduced by about 10.2%, the solar curtailment rate was reduced by 14.2%, and the stakeholder benefits were around 154 million dollars. Further discussion is given on the benefits of shared energy storage investments.

Influence of optimizing prosumers on rural distribution networks

A plethora of factors is currently leading to significant changes in both the planning and operation of distribution networks and systems, such as single-family houses and commercial and industrial buildings. These factors include the increasing share of decentralized energy generation and storage systems in distribution networks, the integration of electric vehicles, the coupling of electrical and thermal systems, as well as the increasing and easy use of Information and Communication Technologies (ICT). The primary actors in this situation, formerly known as consumers of energy and power, have now become prosumers (producers and consumers), who feed energy back into the network at specific times.Hybrid energy systems combine cross-sectoral energy sources and supply systems as well as energy distribution and storage to form one compact system. The aim is to substitute fossil energy sources with primarily renewably generated electricity, other renewable energy sources, and sustainable ways of energy usage. Moreover, the use of new meter functions and services enables market-dependent dynamic or special consumer- and prosumer-oriented tariffs.A simulation model has been developed to model, simulate, and analyze the behavior and steady-state energy flows of such hybrid energy systems. By using linear optimization algorithms, the optimal use of decentralized energy generation and storage systems is determined in the example of a single-family house. The simulation model considers different optimization objectives, desired energy services, external boundary conditions, individual technical components, as well as selected energy tariffs. Furthermore, the simulation model is used to analyze how the high penetration of optimizing prosumers influences rural distribution networks.

Mixed‐integer stochastic evaluation of battery energy storage system integration strategies in distribution systems

This paper presents a new approach to the problem of defining an investment policy in battery energy storage systems in active distribution networks, taking into account a diversity of uncertainties. The proposed methodology allows the selection of type, capacity, and location of battery energy storage systems in distribution networks with distributed generation and electric vehicle charging stations. A mixed-integer stochastic programming problem is cunningly approached with a metaheuristic, where fitness calculation with stochastic scenarios is performed by introducing an approximation to the operation costs in the form of a polynomial neural network, generated according to the Group Method of Data Handling—GMDH method, with strong computing speeding-up. The quality of this approximation for heavy Monte Carlo simulations is assessed in a first case study using a 33-bus distribution test system. The optimization planning model is then validated in the same test system using real data collected from solar and wind sources, demand, prices, and charging stations. Four types of batteries are compared considering degradation impact. The results demonstrate the practicality and advantages of this process.

Optimal Incorporation of Photovoltaic Energy and Battery Energy Storage Systems in Distribution Networks Considering Uncertainties of Demand and Generation

In this paper, the Archimedes optimization algorithm (AOA) is applied as a recent metaheuristic optimization algorithm to reduce energy losses and capture the size of incorporating a battery energy storage system (BESS) and photovoltaics (PV) within a distribution system. AOA is designed with revelation from Archimedes’ principle, an impressive physics law. AOA mimics the attitude of buoyant force applied upward on an object, partially or entirely dipped in liquid, which is relative to the weight of the dislodged liquid. Furthermore, the developed algorithm is evolved for sizing several PVs and BESSs considering the changing demand over time and the probability generation. The studied IEEE 69-bus distribution network system has different types of the load, such as residential, industrial, and commercial loads. The simulation results indicate the robustness of the proposed algorithm for computing the best size of multiple PVs and BESSs with a significant reduction in the power system losses. Additionally, the AOA algorithm has an efficient balancing between the exploration and exploitation phases to avoid the local solutions and go to the best global solutions, compared with other studied algorithms.

Joint investment of community energy storage systems in distribution networks using modified Nash bargaining theory

The emergence of renewable energy technologies in distribution networks and microgrids has raised the importance of integrating energy storage systems into these grids. However, their high investment costs deter decision makers from effectively expanding these assets. In this paper, a cooperative community storage expansion plan is proposed as a bargaining problem between distribution company and private microgrids to jointly invest in energy storage systems. By doing so, each party takes a quota of shared investment costs and thus the burden of high investment costs is alleviated. A modified version of the Nash bargaining theory approach is proposed to implement the cooperative framework in a fair manner. Two cases of non-cooperation and cooperation are distinctively defined and the merits of cooperation are illustrated. The lead acid battery is considered as the storage candidate, for which a novel linearized lifetime model and replacement approach is also proposed. The bargaining results presented on a distribution network test case indicate that through the proposed cooperation, all players receive positive surpluses by decreasing their costs or increasing their revenues. Thus, both distribution company and microgrids would have incentives to participate in the proposed cooperation. Moreover, the superiority of the proposed modified Nash bargaining theory compared to conventional Nash bargaining theory in terms of cooperation fairness is illustrated. Finally, the ability of the proposed cooperative community storage expansion plan to effectively manage cooperative storage installations and replacements is demonstrated.

Multiobjective nested optimization framework for simultaneous integration of multiple photovoltaic and battery energy storage systems in distribution networks

The rapid growth of renewables in modern distribution networks results in the spilling of energy due to the limited hosting capacity of these networks, violation of system constraints, reduced network efficiency, and improper utilization of resources. Battery energy storage system (BESS), in spite of its high cost, a shorter life and complex control, offers a flexible solution for the problem. In this paper, a multiobjective nested optimization framework is developed for the simultaneous optimal allocation of multiple solar photovoltaics (SPVs) and BESSs in the distribution networks. The framework involves a two-layered structure; the outer layer provides tentative planning solutions to the inner layer that optimizes the desired objectives of network operations and then returns the functional values back to the outer layer. The purpose of the inner-layer is to satisfy the operational constraints of the networks and ensure the optimal utilization of BESS capacities, suggested by the outer layer, at the time of planning itself. A new BESS operating strategy is proposed for optimum utilization of BESS. The nested multiobjective optimization problem is handled by suggesting a new weighted sum approach in conjunction with a recently developed swarm intelligence-based algorithm, i.e. moth search optimization. Overall, the proposed deterministic model essentially ensures the high penetration of SPVs and the optimal utilization of BESSs to justify their installation. The optimization model is investigated on a benchmark 33-bus test distribution network. The application results highlight enhanced energy efficiency, peak load shaving, high renewable penetration, voltage profile improvement, and mitigation of reverse power flow while effectively absorbing the excess renewable power generation during light load hours.

Category-Based Multiobjective Approach for Optimal Integration of Distributed Generation and Energy Storage Systems in Distribution Networks

Distributed generation (DG) units are power generating plants that are very important to the architecture of present power system networks. The primary benefits of the addition of these units are to increase the power supply and improve the power quality of a power grid while considering the investment cost and carbon emission cost. Most studies have simultaneously optimized these objectives in a direct way where the objectives are directly infused into the multiobjective framework to produce final values. However, this method may have an unintentional bias towards a particular objective; hence this paper implements a multi-stage framework to handle multiple objectives in a categorical manner to simultaneously integrate DG units and Battery Energy Storage System (BESS) in a distribution network. A new hybrid metaheuristic technique is developed and combined with the Technique Order for Preference by Similarity to Ideal Solution (TOPSIS) approach and the crowding distance technique to produce Pareto optimal solutions from the multiple collective objectives, namely technical, economic, and environmental. Compared to the conventional direct way approach in multiobjective handling, the proposed categorical approach reduces bias towards a set of objective(s) and efficiently handles more objectives. Results also show that the Whale Optimization Algorithm and Genetic Algorithm (WOAGA) produces the smallest power loss of 101.6 kW compared to Whale Optimization Algorithm (WOA) and Genetic Algorithm (GA), which produces 105.1 kW and 105.8 kW respectively. The algorithm, although does not have a faster convergence than the WOA, has a better computational time than the WOA and GA. The multiobjective WOAGA also performs better than the Non-dominating Sorted Genetic Algorithm (NSGA-II) and the multiobjective WOA in terms of the quality of Pareto optimal solutions.

Optimal Scheduling for Energy Storage Systems in Distribution Networks

Distributed energy storage may play a key role in the operation of future low-carbon power systems as they can help to facilitate the provision of the required flexibility to cope with the intermittency and volatility featured by renewable generation. Within this context, this paper addresses an optimization methodology that will allow managing distributed storage systems of different technology and characteristics in a specific distribution network, taking into account not only the technical aspects of the network and the storage systems but also the uncertainties linked to demand and renewable energy variability. The implementation of the proposed methodology will allow facilitating the integration of energy storage systems within future smart grids. This paper’s results demonstrate numerically the good performance of the developed methodology.

Two-Layer Optimization Model for the Siting and Sizing of Energy Storage Systems in Distribution Networks

One of the most important issues that must be taken into consideration during the planning of energy storage systems (ESSs) is improving distribution network economy, reliability, and stability. This paper presents a two-layer optimization model to determine the optimal siting and sizing of ESSs in the distribution network and their best compromise between the real power loss, voltage stability margin, and the application cost of ESSs. Thereinto, an improved bat algorithm based on non-dominated sorting (NSIBA), as an outer layer optimization model, is employed to obtain the Pareto optimal solution set to offer a group of feasible plans for an internal optimization model. According to these feasible plans, the method of fuzzy entropy weight of vague set, as an internal optimization model, is applied to obtain the synthetic priority of Pareto solutions for planning the optimal siting and sizing of ESSs. By this means, the adopted fuzzy entropy weight method is used to obtain the objective function’s weights and vague set method to choose the solution of planning ESSs’ optimal siting and sizing. The proposed method is tested on a real 26-bus distribution system, and the results prove that the proposed method exhibits higher capability and efficiency in finding optimum solutions.

Chance-Constrained Frequency Regulation with Energy Storage Systems in Distribution Networks

One of the applications of energy storage systems (ESSs) is to support frequency regulation in power systems. In this paper, we consider such an application and address the challenges of uncertain frequency changes, limited energy storage, as well as distribution network constraints. We formulate a bi-level optimization problem that includes the operation objectives of the system operator and the ESSs, using chance constraints to account for uncertain frequency changes. The frequency regulation decision of the system operator depends on the ESSs decision to participate in the regulation service as well as the distribution network constraints. Due to the interdependencies between the ESSs demand fluctuations and the distribution network power flow changes, the system operator requires the ESSs’ operation information for frequency regulation decisions, which may not be available from the ESSs. Therefore, we propose a decentralized algorithm such that the system operator and the ESSs can pursue their own operation objectives, while ensuring the distribution network constraints are satisfied. We evaluate the performance of our method on IEEE 37-bus and 123-bus test feeders by considering combinations of ESSs with different sizes. Simulation results demonstrate that our approach can successfully coordinate the ESSs to regulate the frequency deviations.

Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm

The deployment of utility-scale energy storage systems (ESSs) can be a significant avenue for improving the performance of distribution networks. An optimally placed ESS can reduce power losses and line loading, mitigate peak network demand, improve voltage profile, and in some cases contribute to the network fault level diagnosis. This paper proposes a strategy for optimal placement of distributed ESSs in distribution networks to minimize voltage deviation, line loading, and power losses. The optimal placement of distributed ESSs is investigated in a medium voltage IEEE-33 bus distribution system, which is influenced by a high penetration of renewable (solar and wind) distributed generation, for two scenarios: (1) with a uniform ESS size and (2) with non-uniform ESS sizes. System models for the proposed implementations are developed, analyzed, and tested using DIgSILENT PowerFactory. The artificial bee colony optimization approach is employed to optimize the objective function parameters through a Python script automating simulation events in PowerFactory. The optimization results, obtained from the artificial bee colony approach, are also compared with the use of a particle swarm optimization algorithm. The simulation results suggest that the proposed ESS placement approach can successfully achieve the objectives of voltage profile improvement, line loading minimization, and power loss reduction, and thereby significantly improve distribution network performance.

Integration of storage systems in distribution networks through multiobjective optimization

The use of storage systems in distribution networks allows smoothing the load diagram. In fact, the cost of energy is different along the day and companies can be encouraged to use these systems, since the extra energy required to charge the storage system can be obtained in periods where the cost of the energy is lower and used in periods when the energy cost is higher. Storage systems also allow reducing losses of the lines and improving voltage profile. However, in distribution networks there are benefits in using distributed storage instead of centralized storage. Under this context, this paper proposes a multiobjective optimization approach for the location and sizing of storage systems. In this problem, the objective functions are in conflict. Increasing the number of storage systems leads to a reduction in the peak power and losses, but also will increase the investment cost. This approach allows obtaining solutions of different trade-offs with respect to the two objectives. An IEEE 69 buses and a real 94 buses test feeders are used to demonstrate the effectiveness of the proposed approach.

Simultaneous Planning of Renewable/ Non-Renewable Distributed Generation Units and Energy Storage Systems in Distribution Networks

The increased diversity of different types of energy sources requires moving towards smart distribution networks. This paper proposes a probabilistic DG (distributed generation) units planning model to determine technology type, capacity and location of DG units while simultaneously allocating ESS (energy storage systems) based on predetermined capacities. This problem is studied in a wind integrated power system considering loads, prices and wind power generation uncertainties. A suitable method for DG unit planning will reduce costs and improve reliability concerns. Objective function is a cost function that minimizes DG investment and operational cost, purchased energy costs from upstream networks, the defined cost to reliability index, energy losses and the investment and degradation costs of ESS. Electrical load is a time variable and the model simulates a typical radial network successfully. The proposed model was solved using the DICOPT solver under GAMS optimization software.