Energy Storage Allocation Research Articles

Optimal Energy Storage Allocation for Combined Wind-PV-EVs-ES System Based on Improved Triangulation Topology Aggregation Optimizer

To determine the ES allocation based on a specific number of EVs connected to a combined WPESS, this paper develops an ESS allocation model that considers the impact of EV charging behavior on LSD, ES allocation cost, new energy utilization rate, and self-power rate. First, several scenarios are generated using Monte Carlo sampling (MCS), and a typical day is selected through Backward Reduction (BR). Next, the Monte Carlo method is employed to generate conventional EV charging curves and optimize EV charging behavior by considering LSD and user charging costs. Subsequently, an ES capacity allocation model is developed, considering system costs, new energy utilization rate, and self-power rate. Finally, an improved triangulation topology aggregation optimizer (TTAO) is proposed, incorporating the logistic map, Golden Sine Algorithm (Gold-SA) strategy, and lens inverse imaging learning strategy. These enhancements improve the algorithm’s ability to identify global optimal solutions and facilitate its escape from local optima, significantly enhancing the optimization effectiveness of TTAO. The analysis of the calculation example indicates that after optimizing the charging behavior of EVs, the average daily cost is reduced by 204.94, the self-power rate increases by 2.25%, and the utilization rate of new energy sources rises by 2.50%, all while maintaining the same ES capacity.

Stochastic optimal allocation of grid-side independent energy storage considering energy storage participating in multi-market trading operation

The integration of large-scale intermittent renewable energy generation into the power grid imposes challenges to the secure and economic operation of the system, and energy storage (ES) can effectively mitigate this problem as a flexible resource. However, the conventional ES allocation is mostly planned to meet the regulation demands of individual entities, which is likely to result in low utilization of ES and difficult to recover the investment cost. Therefore, a two-stage stochastic optimal allocation model for grid-side independent ES (IES) considering ES participating in the operation of multi-market trading, such as peak-valley arbitrage, frequency regulation, and leasing, is proposed in this paper to improve the comprehensive benefits and utilization rate of ES. The first stage aims to allocate IES and develop a systematic scheduling plan based on the forecast of wind power output and load demand, while the second stage responds to the uncertainty of wind power output by re-dispatching generating units and invoking ES power leased by wind farms. Then, a two-layer loop iterative solution algorithm based on the Benders decomposition is formed to effectively solve the proposed model. Finally, the approach developed in this paper is applied to a modified IEEE RTS-79 test system, and the results verify that it is both feasible and effective.

Efficient allocation of energy storage and renewable energy system for performance and reliability improvement of distribution system

This paper aims to investigate the impact of optimal planning of renewable energy systems and independent battery energy storage systems (BESS) on the performance and reliability of distribution systems (DS). A unique energy management scheme (EMS) controls the charging and discharging schedules of the BESS. The transit search optimization (TSO) algorithm determines the optimal allocation of wind turbine (WT) and solar photovoltaic (SPV) generator units, along with BESS units. The optimization focuses on improving the DS reliability in terms of energy supply, increasing the feeder security margin, and reducing apparent power loss. Multiple case studies are designed to study the efficacy of the problem outcomes and are validated using Bus 4 of the Roy Bollington Test System (RBTS). To obtain results that are close to actual operating conditions, the modelling process takes into account the time-varying load profile and the dynamic power outputs from the SPV and WT units. The findings show that the suggested method works well by enhancing the feeder security margin by a maximum of 11.25%, reducing expected energy not supplied (EENS) by 4.3%, and improving the power loss profile by 11.5%. In addition, the feeder upgrade deferral (FUD) years also show a maximum improvement of 20.8%.

Optimal configuration method of photovoltaic energy storage in distribution network based on source-load coordination

Abstract To enhance the configurability of photovoltaic energy storage within distribution network systems and foster synchronized development of power sources and loads, a source-load coordinated approach for optimal photovoltaic energy storage configuration in distribution networks is introduced. An alternative multi-objective framework for optimal allocation of photovoltaic energy storage capacity in distribution networks is formulated, which is the optimal goal of maximum economic benefit of photovoltaic energy storage, the optimal goal of minimum network loss and the optimal goal of source-network load coordination. The constraints of the system’s power flow, energy storage charging and discharging capabilities, and an optimized allocation strategy for energy storage are established, and the objective function is solved with full consideration of source-network load coordination factors. The energy storage optimization is updated in the iterative process. Based on the update results, the process for optimal allocation of photovoltaic energy storage in the distribution network has been devised to attain the most efficient allocation. Experimental results indicate a minimal discrepancy between the actual and specified energy storage output, along with a reduced average output power resulting from the optimized photovoltaic energy storage configuration, which shows excellent performance in energy storage optimization configuration.

An improved moth flame optimization for optimal DG and battery energy storage allocation in distribution systems

Deploying distributed generators (DGs) powered by renewable energy poses a significant challenge for effective power system operation. Optimally scheduling DGs, especially photovoltaic (PV) systems and wind turbines (WTs), is critical because of the unpredictable nature of wind speed and solar radiation. These intermittencies have posed considerable challenges to power grids, including power oscillation, increased losses, and voltage instability. To overcome these challenges, the battery energy storage (BES) system supports the PV unit, while the biomass aids the WT unit, mitigating power fluctuations and boosting supply continuity. Therefore, the main innovation of this study is presenting an improved moth flame optimization algorithm (IMFO) to capture the optimal scheduling of multiple dispatchable and non-dispatchable DGs for mitigating energy loss in power grids, considering different dynamic load characteristics. The IMFO algorithm comprises a new update position expression based on a roulette wheel selection strategy as well as Gaussian barebones (GB) and quasi-opposite-based learning (QOBL) mechanisms to enhance exploitation capability, global convergence rate, and solution precision. The IMFO algorithm's success rate and effectiveness are evaluated using 23rd benchmark functions and compared with the basic MFO algorithm and other seven competitors using rigorous statistical analysis. The developed optimizer is then adopted to study the performance of the 69-bus and 118-bus distribution grids, considering deterministic and stochastic DG's optimal planning. The findings reflect the superiority of the developed algorithm against its rivals, emphasizing the influence of load types and varying generations in DG planning. Numerically, the optimal deployment of BES + PV and biomass + WT significantly maximizes the energy loss reduction percent to 68.3471 and 98.0449 for the 69-bus's commercial load type and to 54.833 and 52.0623 for the 118-bus's commercial load type, respectively, confirming the efficacy of the developed algorithm for maximizing the performance of distribution systems in diverse situations.

Optimal Allocation of Hybrid Energy Storage Capacity Based on ISSA-Optimized VMD Parameters

To address the issue where the grid integration of renewable energy field stations may exacerbate the power fluctuation in tie-line agreements and jeopardize safe grid operation, we propose a hybrid energy storage system (HESS) capacity allocation optimization method based on variational mode decomposition (VMD) and a multi-strategy improved salp swarm algorithm (ISSA). From typical wind load power and contact line agreement power, the HESS power is obtained. VMD decomposes this power into high- and low-frequency power, respectively, for the super capacitor and the Li-ion battery. Considering charging and discharging power and state of charge (SOC) constraints, an optimization model minimizing the system equivalent annual value cost is established. ISSA optimizes the best decomposition layer K and penalty coefficients α in VMD. The optimal cut-off point and corresponding energy storage allocation scheme are analyzed. A simulation and analysis on MATLAB show that the proposed ISSA-VMD HESS capacity allocation scheme saves 7.53% in costs compared to an empirical mode decomposition (EMD) scheme, proving the method’s effectiveness and superiority.

Optimal allocation of offshore wind power and energy storage considering source-load power stochasticity

Abstract Large-scale offshore wind generation has been integrated to power grids in China. The annual increase in electric vehicles, air conditioning systems, and other electrical facilities has intensified the randomness and volatility of power supply and demand, presenting significant challenges to the safe and economical operation of power systems. Energy storage systems serve as regulators in the power grid, yet the electrical performance and costs associated with various storage technologies differ considerably. Consequently, the strategic planning and development of energy storage solutions tailored to specific local conditions have emerged as critical areas of research. This paper presents an in-depth analysis of power characteristics across source loads, explores an optimized configuration approach for energy storage, and validates this method through a numerical example. The findings confirm the effectiveness of the proposed configuration strategy and offer pertinent recommendations for the implementation of energy storage solutions.

Subsidy Policies and Economic Analysis of Photovoltaic Energy Storage Integration in China

In the context of China’s new power system, various regions have implemented policies mandating the integration of new energy sources with energy storage, while also introducing subsidies to alleviate project cost pressures. Currently, there is a lack of subsidy analysis for photovoltaic energy storage integration projects. In order to systematically assess the economic viability of photovoltaic energy storage integration projects after considering energy storage subsidies, this paper reviews relevant policies in the Chinese photovoltaic energy storage market. It analyzes the cost and revenue composition of photovoltaic energy storage integration projects, and constructs a system dynamics model for the levelized cost of electricity (LCOE) of such projects. Taking a specific photovoltaic energy storage project as an example, this paper measures the levelized cost of electricity and the investment return rate under different energy storage scenarios. Combining energy storage allocation ratios and internal rate of return indicators, this paper analyzes the net present value of photovoltaic energy storage integration projects under different subsidy standards. The results indicate that, while the current energy storage subsidy policies positively stimulate photovoltaic energy storage integration projects, they exhibit a limited capacity to cover energy storage investment costs, thereby failing to incentivize capital market participation in the construction of such projects. Rational allocation of energy storage capacity and optimization of corresponding subsidy policies are crucial prerequisites for enhancing the economic viability and widespread adoption of photovoltaic energy storage integration projects. This study not only aids in investment decision making for photovoltaic power stations but also contributes to the formulation of energy storage subsidy policies.

Research on the participation model of energy storage in electricity spot markets considering energy constraints and cost characteristics

In the context of power systems with a high proportion of renewable energy, energy storage plays a significant role in facilitating the consumption of renewable energy and ensuring the operational safety of power systems. However, the current power spot market's predominant power bidding model does not fully consider the physical and cost-operational characteristics of energy storage, which is not conducive to further incentivizing investment and construction of energy storage, and may indirectly affect the flexibility of energy storage in peak shaving and valley filling. This paper summarizes the key issues that need to be addressed for energy storage to participate in the spot market from two aspects: the power bidding model does not meet the requirements of the physical and cost-operational characteristics of energy storage, and the real-time market under this model cannot achieve optimal allocation of energy storage. Considering the energy constraints and cost characteristics of energy storage, a charge and discharge bidding model is proposed, which is based on the stored energy value of energy storage and is in line with the physical and cost-operational characteristics and real-time optimization needs of energy storage. Subsequently, a market clearing model for energy storage participation in the spot market under the state of energy bidding method is constructed, and based on the IEEE 39-bus test case, a comparative analysis of the nodal electricity prices, energy storage revenue, and total system costs under the proposed market participation model and the traditional power bidding model is conducted. Simulation results show that the proposed energy storage participation model in the spot market can better utilize the value of energy storage in peak shaving and valley filling compared to the conventional power bidding model, reducing the extreme electricity prices by up to 10%, increasing single cycle revenue of energy storage by 46%, and reducing the total operating costs of the system in scenarios with significant deviations in system load in the day-ahead and real-time markets.

Optimization of Frequency Modulation Energy Storage Configuration in Power Grid Based on Equivalent Full Cycle Model

This paper aims to meet the challenges of large-scale access to renewable energy and increasingly complex power grid structure, and deeply discusses the application value of energy storage configuration optimization scheme in power grid frequency modulation. Based on the equivalent full cycle model and a large number of actual operation data, various energy storage technologies are technically analyzed, and the economic and environmental performance of different energy storage configuration schemes are comprehensively evaluated. On this basis, this paper puts forward a set of efficient and economical energy storage configuration optimization strategies to meet the demand of power grid frequency modulation and promote the wide application of energy storage technology. After an in-depth analysis, it is found that the optimized energy storage configuration scheme is excellent in technology, economy, and environmental protection. Specifically, in terms of technical performance, the optimization scheme has significantly improved key indicators such as energy storage efficiency, capacity and power, and response speed, which can better meet the requirements of power grid frequency modulation. Through the verification of actual operation data, it is found that the overall efficiency of the optimized energy storage configuration scheme is above 55%, which is helpful to the stability and efficiency of power grid frequency modulation. In terms of economic performance, although the initial investment cost of the optimization scheme may be high, it is found that it has good economy through the evaluation of long-term operation benefits. Considering that the energy storage system can reduce the operating cost of the power grid, improve the energy utilization rate, and achieve the optimization of cost-effectiveness in the long run, this scheme is economically feasible and attractive. In terms of environmental performance, the optimization scheme effectively reduces the negative impact on the environment by improving energy storage efficiency, reducing emissions, and optimizing resource utilization. This is not only conducive to the sustainable development of the power grid but also in line with the current global trend of promoting green and low-carbon transformation. To sum up, this paper not only provides an efficient and economical energy storage allocation optimization strategy for power grid frequency modulation but also provides a scientific basis for relevant decision-making departments. By promoting the practical application and development of energy storage technology, this paper is helpful to improve the frequency modulation ability of power grid, optimize energy structure, and reduce environmental pollution, and thus achieve the goal of sustainable energy development. The data results and in-depth analysis of this paper provide strong support for the practical application of energy storage configuration optimization scheme and also provide important reference for the further innovation and development of energy storage technology.

Optimal allocation of customer energy storage based on power big data and improved LSTM load forecasting

This research explores the potential of energy storage investment with a focus on regional power users. An incentive-based demand response framework is constructed, emphasizing the aggregation of demand-side resources for active participation in the power market. Utilizing a data-driven approach, the improved Long Short Term Memory (LSTM) model is employed to predict customer behavior in response to incentives. The primary objective is to maximize the life cycle benefit while minimizing the payback period for users investing in energy storage. By harnessing big data analytics, suitable users for energy storage investment are identified and optimal capacity allocation is determined. Given the current energy storage parameters and dynamics of electricity pricing, boundary values for profitable user energy storage capacities are derived. This methodology filters users with capacities exceeding these thresholds, marking them as potential candidates for energy storage deployment. Furthermore, a measurement model for iron phosphate batteries is introduced. Analysis of adjustable resource capacity, duration, and benefits for potential users provides insights into optimal energy storage investment strategies. Integrating configured energy storage batteries with time-of-use tariffs substantially reduces energy storage costs, leading to enhanced economic efficiency.

Optimal allocation of multiple energy storage in the integrated energy system of a coastal nearly zero energy community considering energy storage priorities

Energy storage technologies play a vital role in the low-carbon transition of the building energy sector. However, integrating multiple energy storage (MES) into integrated energy system (IES) in high-demand coastal communities remains a challenging task. This study proposes a novel regional IES that incorporates batteries, compressed air energy storage, and thermal energy storage for the simulated coastal community in Hong Kong; then developed the multi-objective optimization considering matching, economic, and environmental performance on MES capacity allocation with specially consideration of three energy management strategies under different energy storage priorities. The results show that all three storage priorities perform favorable outcomes across all objectives; compared to a reference scenario without storage, they achieve a relative net present value (NPVr) over 40 million HKD, a 0.29 improvement in matching performance, and over 2600 tCO2 in carbon emission reduction. Moreover, the renewable energy consumption rate exceeds 90 %, and the annual electricity import demand reduction rate exceeds 50 %. Sensitivity analysis highlights that larger energy storage capacities do not necessarily yield better outcomes, emphasizing the importance of synergistic interactions among multiple storage technologies. The presented energy system with MES and the optimal allocation approach herein may provide valuable insights for future energy planning in nearly-zero energy communities.

Hybrid energy storage configuration method for wind power microgrid based on EMD decomposition and two-stage robust approach

Data centers are usually characterized by high energy loads, which raises increasing sustainability concerns in both academic and daily usage. To mitigate the uncertainty and high volatility of distributed wind energy generation, this paper proposes a hybrid energy storage allocation strategy by means of the Empirical Mode Decomposition (EMD) technique and the two-stage robust method. First, this paper conducts the evolution analyses for the over- and under-evaluated uncertainty of wind power fluctuation under different time scales. Second, we employ the EMD technique to configure a high-frequency flywheel energy storage device, realizing the wind power transformation from large fluctuations to small fluctuations and the convergence of the wind power fluctuation curves in minute- and hour levels. Finally, based on the hour-level wind energy stable power curves, we carry out two-stage robust planning for the equipment capacity of low-frequency cold storage tanks and lithium bromide chillers. The case study on a data center microgrid in northeastern China confirms the effectiveness of our proposed strategy.

Study on optimal allocation of energy storage in multi-regional integrated energy system considering stepped carbon trading

Abstract In this study, an energy storage configuration optimization model of multi regional integrated energy system based on integrated scheduling and stepped Carbon emission trading is proposed. By analyzing the steady-state, dynamic, and variable operating characteristics of multi regional integrated energy systems, a distributed energy planning modeling method was adopted to construct energy storage configuration constraint objects and optimization models. The target scheme of energy storage configuration is optimized by using the results of integrated scheduling scheme and dynamic distribution analysis of ladder Carbon emission trading, and the parameters are optimally estimated by using Monte Carlo method and quadratic fitting algorithm. The simulation results show that this method has a strong scheduling capability in the context of stepped Carbon emission trading, can accurately assess the load demand and supply demand relationship, reduce carbon emissions, and improve energy efficiency, with an average of 0.9121.

Risk control of hydropower-photovoltaic multi-energy complementary scheduling based on energy storage allocation

The complementary scheduling of hydropower with wind and photovoltaic (PV) power is an effective way to promote new energy consumption. However, previous studies have disregarded the operational risks of hydropower plants due to their physical constraints when complementing new energy sources. This study proposes a risk control method for a hybrid hydro-PV power system by adding electrochemistry energy storage (EES). A “day ahead–intraday–real-time” three-layer nested model is developed to guide the joint operation of the hydro-PV-EES hybrid system. The complementary flexibility of hydropower and EES is quantified and utilized to minimize unfavorable operating conditions of hydropower units when regulating PV forecast deviations. A case study of China's Longyangxia hydro-PV complementary project shows that after EES is connected to the system: (1) the total power generation of the system is improved 3.04%, and no power curtailment or shortage occurs; (2) the number of startups and shutdowns, and crossing the vibration zone of hydropower units are reduced by 12.87% and 12.17%, thereby improving the safety and operational stability of the hydropower plant; (3) the regulation flexibility supply of the complementary system is considerably improved; and (4) EES achieves flexible and safe control itself, with over 90% of the scheduling periods being in a healthy SOC range.

Optimal participation and cost allocation of shared energy storage considering customer directrix load demand response

In recent years, with the increase in the proportion of new energy connected to the grid, the main goal of energy storage on the load side and energy storage users is to maximize the overall interests. Based on the poor utilization ratio and high use cost of energy storage configured on the user side, the controllability of adjustable load and the rationality of energy storage configuration are two key points that need to be considered for social welfare maximization (SWM). Hence, this paper puts forward an implementation method of large-scale demand response (DR) based on the customer directrix load (CDL), in order to give full play to the DR of the load side and improve the utilization rate of shared energy storage (SES). First, the CDL proposed in this paper gives the full-time DR regulation target, which can better guide load shaping and solve the problem of flexible load controllability. Then, an SES configuration model based on SWM is built to further shape the load and improve the utilization rate and economy of energy storage. In addition, in the market with incomplete free competition, a recent market clearing strategy based on Vickery-Clark-groves (VCG) is proposed, which encourages market members to declare true information to achieve the global optimal effect and realize the sustainability of the proposed model. The simulation results show that with the participation of SES, the similarity of users based on CDL has increased by 8.59 % and 0.99 % respectively, and the social welfare has reached 41,297.71 yuan/day. The results also show that the reasonable cost allocation through the VCG mechanism shortens the investment return period of SES by 33.33 %, which promotes the investment enthusiasm of SES.

Net-zero energy transition in ASEAN countries: The evolutionary model brings novel perspectives to the cooperative mechanism of climate governance

In an era marked by escalating climate change, the fragile ecological balance faces increasing strain. Whilst significant knowledge exists regarding the accumulation of carbon emission within the Association of Southeast Asian Nations, little is known about when and how countries could reach net-zero emission goal as agreed in Paris Agreement. For this purpose, our study examines the primary driving factors of carbon emission from 1990 to 2020 across the ten countries using the Logarithmic Mean Divisia Index model. We leverage the random forest model to explore the net-zero scenarios and the Autoregressive Integrated Moving Average approach to identify the evolutionary trajectories of carbon emission trends. Our findings underscore the imperative need for expediting decarbonization efforts, emphasizing the urgency for widespread adoption of clean technologies and substantial investment in green initiatives. Countries at similar stages of progress might establish a cooperation mechanism of clean energy base construction, energy storage allocation and policy formulation. These insights can help us better estimate future demand of clean energy, explore strategies for decarbonization, and inform historical commonalities of carbon emission growth.

Optimal Capacity Allocation of Seasonal Energy Storage for High-Proportion Renewable Energy System Considering ENSO Events

Optimal Capacity Allocation of Seasonal Energy Storage for High-Proportion Renewable Energy System Considering ENSO Events

Peer-to-peer energy sharing model considering multi-objective optimal allocation of shared energy storage in a multi-microgrid system

A novel peer-to-peer (P2P) energy sharing model incorporating shared energy storage (SES) is proposed in order to effectively utilize renewable energy sources and facilitate flexible energy trading among microgrids. The model is divided into three main blocks. In the first block, a multi-objective optimal allocation scheme for SES is developed to maximize SES benefits, minimize carbon emissions, and enhance the sustainability of the energy sharing zone (ESZ). The second block introduces a new internal pricing mechanism based on the energy supply and demand ratio. Further, an innovative optimal scheduling strategy for the ESZ is presented in the third block, which includes demand response strategies for residential microgrids and control strategies for SES, with the goal of minimizing the energy cost of a multi-microgrid system (MMS). To solve the proposed complex and constrained model, a multi-objective slime mould algorithm based on generalized opposition-based learning and reference points is proposed. Simulation results demonstrate that the introduction of SES and energy sharing can reduce MMS's energy cost by 89.45 % and increase the sustainability index of ESZ by 9.08 %. The proposed P2P trading mechanism can strictly lock the internal trading prices within the grid prices, which fully mobilizes the microgrids to participate in P2P trading.

Optimization of Energy Storage Allocation in Wind Energy Storage Combined System Based on Improved Sand Cat Swarm Optimization Algorithm

In order to improve the operation reliability and new energy consumption rate of the combined wind–solar storage system, an optimal allocation method for the capacity of the energy storage system (ESS) based on the improved sand cat swarm optimization algorithm is proposed. First, based on the structural analysis of the combined system, an optimization model of energy storage configuration is established with the objectives of the lowest total investment cost of the ESS, the lowest load loss rate and the lowest new energy abandonment rate, which not only takes into account the economy of energy storage construction for investors and builders, but also reduces the probability of blackout for users to protect their interests and improves the utilization rate of the natural resources of wind and light, which can achieve a multi-win–win situation. The model can realize the win–win situation in many aspects. Secondly, an improved k-means clustering algorithm is used to cluster the renewable energy power and load data to realize the typical day data extraction. Then, for the proposed multi-objective optimization model, an SCSO is proposed based on the triangular wandering strategy, Lévy flight strategy and lens imaging reverse learning improvement, which can help the algorithm to jump out of the local optimum while improving its global optimization ability, and these improvements can significantly improve the optimization effect of the SCSO. Finally, simulation analysis is carried out in combination with typical daily extraction data, and the results verify the advantages and effectiveness of the proposed model and algorithm.