Optimal Capacity Allocation Research Articles

Capacity Optimization of Wind–Solar–Storage Multi-Power Microgrid Based on Two-Layer Model and an Improved Snake Optimization Algorithm

A two-layer optimization model and an improved snake optimization algorithm (ISOA) are proposed to solve the capacity optimization problem of wind–solar–storage multi-power microgrids in the whole life cycle. In the upper optimization model, the wind–solar–storage capacity optimization model is established. It takes wind–solar power supply and storage capacity as decision variables and the construction cost of the whole life cycle as the objective function. At the lower level, the optimal scheduling model is established, considering the output characteristics of various types of power supplies and energy storage, microgrid sales, and purchases of power as constraints. At the same time, the model considers constraints, such as the power balance, the operating state of the energy storage system, the power sales and purchases, and the network fluctuations, to ensure the system operates efficiently. Taking a microgrid in South China as an application scenario, the model is solved and the optimal capacity allocation scheme of the microgrid is obtained. Meanwhile, the demand response mechanism and the influence of planning years are introduced to further optimize the configuration scheme, and the impact of different rigid–flexible load ratios and various planning horizons on microgrid capacity optimization is analyzed, respectively, by a numerical example. The comparison shows that the ISOA has better optimization performance in solving the proposed two-layer model.

Optimal capacity allocation and scheduling strategy for CSP+PV hybrid standalone power plants

Solar photovoltaic (PV) power generation, as an important clean technology, has been widely adopted globally, especially in remote island areas where access to the main power grid is unavailable. PV power can serve as the primary energy source for standalone island grids. However, due to its dependence on sunlight, PV power output fluctuates, particularly during nighttime and under poor lighting conditions, necessitating the integration of energy storage technology or alternative power generation methods to ensure continuous power supply. Concentrating solar power (CSP) generation, as an emerging technology, can provide efficient power output when solar radiation is abundant and ensure continuous power supply through thermal energy storage systems during adverse weather conditions or nighttime. Although CSP offers advantages such as dispatchability, its high construction and maintenance costs may pose challenges in commercial deployment. Hybrid solar power plants combining both PV and CSP technologies leverage the strengths of both, ensuring more stable and economically viable power output. This study establishes a model for hybrid solar power plants, considering the impact of PV and CSP component capacities and proportions on performance and costs, i.e., capacity allocation. To maximize the overall benefits of standalone microgrids while ensuring the stability of the power station, a capacity allocation method guided by economic dispatch is proposed. Through iterative analysis, the optimal configuration is determined to minimize the system's equivalent annual costs. Simulation experiments validate the financial feasibility of hybrid solar power plants and the reliability of the proposed configuration method.

Capacity Optimization Allocation of Multi-Energy-Coupled Integrated Energy System Based on Energy Storage Priority Strategy

As the global focus on environmental conservation and energy stability intensifies, enhancing energy efficiency and mitigating pollution emissions have emerged as pivotal issues that cannot be overlooked. In order to make a multi-energy-coupled integrated energy system (IES) that can meet the demand of load diversity under low-carbon economic operation, an optimal capacity allocation model of an electricity–heat–hydrogen multi-energy-coupled IES is proposed, with the objectives of minimizing operating costs and pollutant emissions and minimizing peak-to-valley loads on the grid side. Different Energy management strategies with different storage priorities are proposed, and the proposed NSNGO algorithm is used to solve the above model. The results show that the total profit after optimization is 5.91% higher on average compared to the comparison type, and the pollutant emission scalar function is reduced by 980.64 (g), which is 7.48% lower. The peak–valley difference of the regional power system before optimization is 0.5952, and the peak–valley difference of the regional power system after optimization is 0.4142, which is reduced by 30.40%, and the proposed capacity allocation method can realize the economic operation of the multi-energy-coupled integrated energy system.

Optimal Capacity Allocation for Life Cycle Multiobjective Integrated Energy Systems Considering Capacity Tariffs and Eco-Indicator 99

Traditional energy systems pose a significant threat to human social development due to fossil fuel depletion and environmental pollution. Integrated energy systems (IESs) are widely studied and applied due to their clean and low-carbon characteristics to achieve sustainable development. However, as integrated energy systems expand, their impact on ecosystems becomes more pronounced. This paper introduces the concept of the ecological damage index (EDI) to promote the sustainable development of integrated energy systems. Moreover, the introduction of a capacity tariff mechanism will impact the energy structure, making it essential to consider its effects on capacity allocation within integrated energy systems. This paper proposes a multiobjective optimization framework for constructing a capacity planning model for integrated energy systems, focusing on achieving a multidimensional balance between the economy, environment, and ecosystem using the life cycle assessment (LCA) method. Finally, the nondominated sorting genetic algorithm-II (NSGA-II) is employed to optimize the three objectives and obtain the Pareto frontier solution set. The optimal solution is selected from the solution set by combining the technique for order preference by similarity to ideal solution (TOPSIS) and Shannon entropy method. In comparison to scenarios with incomplete considerations, the multiobjective capacity optimization model proposed in this study exhibits significant improvements across the three metrics of cost, carbon emissions, and the ecological damage index, with a 19.05% reduction in costs, a 26.24% decrease in carbon emissions, and an 8.85% decrease in the ecological damage index. The study demonstrates that the model abandons traditional single-objective research methods by incorporating a multidimensional balance of the economy, environment, and ecosystems. This approach forms a foundational basis for selecting the optimal energy mix and achieving sustainable development in integrated energy systems. The life cycle assessment methodology evaluates impacts across all stages of integrated energy systems, providing a comprehensive basis for assessing and planning the sustainable development of the systems. The study offers guidance for the rational allocation of the integrated energy system capacity and advances the sustainable development of such systems.

Stochastic Risk-driven Bidding of a Solar and Storage Aggregator in Primary Frequency and Energy Markets: A Performance-based Capacity Allocation Approach

The ever-increasing impact of deploying renewable energy resources reducing power system inertia, requires distributed energy resource (DER) aggregators to secure high-performance, fast-responding primary frequency reserve (PFR) in ancillary service markets. However, enabling immediate and local regulation of the operating point upon detecting frequency deviations imposes the necessity of allocating specific headroom for DERs. The individual headroom susceptibility to uncertainty and response speed of a certain DER not only compromises aggregated headroom performance in the PFR market but also leads to incurring unnecessary opportunity costs in the energy market and inadequate frequency control in the power system. To address this challenge, this paper introduces an integrated probabilistic performance index for DERs that considers their individual interactive heterogeneous response speed and uncertainty. Using this index, aggregators overseeing photovoltaic and smart buildings equipped with energy storage can evaluate, allocate, and remunerate optimal headroom capacity for individual DERs based on their performance. This approach ensures maximum aggregated profits and minimum opportunity costs in the PFR and energy markets while improving the performance of the provided PFR capacity, respectively. These tasks are aimed by a novel optimal capacity allocation strategy for an aggregator in both markets. This strategy employs a mixed-integer linear programming model to find the optimal solution and the conditional value-at-risk measure to tackle the uncertainties faced with the problem. The efficiency of the proposed model is assessed within a system reflecting the ERCOT market structure, demonstrating improved aggregated headroom performance, reduced energy opportunity costs, diminished risk of capacity shortage, and improved load frequency control (LFC) from the power system perspective.

Optimization of shared energy storage configuration for village-level photovoltaic systems considering vehicle charging management

Distributed renewable energy is more abundant in rural areas, and a large amount of distributed photovoltaic grid-connected power brings challenges to the stable of the power grid. How to promote the self-generation and self-consumption of distributed renewable energy has become an urgent problem. In this paper, a village-level distributed photovoltaic power generation system including energy storage and electric vehicles is constructed. With the goal of minimizing the photovoltaic grid-connected power and maximizing the annual comprehensive revenue, the planning model of energy storage capacity allocation for village-level distributed power generation system is constructed. Considering the charging management for different numbers of electric vehicles, the optimal energy storage capacity allocation strategy is solved using the improved particle swarm algorithm.Five scenarios are set up as examples to be analyzed.The conclusions are:(1)After the configuration of a reasonable energy storage,the grid-connected generation of the system is significantly reduced,and the annual comprehensive revenue of the system is significantly increased.(2)After considering charging management for different numbers of electric vehicles, the capacity and power of the energy storage configuration can be significantly reduced, thus reducing the energy storage investment cost and operation and maintenance cost, and further significantly improving the annual comprehensive revenue of this system.

Dynamic delay-based game approach in car and petrol supply chains under government intervention

Effective decision-making in a temporal context is challenged by the enduring impacts of delays, where decisions made in a certain period are influenced by choices made in preceding periods. This paper delves into the analysis of decision-making in a dynamic game system characterized by delay impact on decisions. Notably, it pioneers the application of a game approach to detect the intricate relationship between car and petrol supply chains operating under conditions of dynamism and uncertainty. The marginal profit method is applied to deal with the dynamic game model. Furthermore, this article investigates the improvement of green car levels, on member profits. In the car supply chain, the car manufacturer sells two distinct product types: green and regular. In this paper, green cars refer to cars with low petrol consumption, and as gas consumption decreases, the green level of the car ascends. This study marks the first instance where optimal production capacity allocation is examined within the framework of delay and uncertainty. Considering the government's initiative to curtail petrol consumption in automobiles, the effects of the government's supporting role are also analyzed in this paper. As a result, the greenness level witnessed a 4.1-fold increase, leading to an 81.7% surge in the demand for green cars, consequently boosting the profit of the entire car supply chain. Several numerical experiments of a real case were examined to evaluate the performance of the proposed models. Finally, some sensitivity analyses were applied to the principal parameters of the model to extract managerial insights.

Optimal Coordinated Operation for Hydro–Wind Power System

The intermittent and stochastic characteristics of wind power pose a higher demand on the complementarity of hydropower. Studying the optimal coordinated operation of hydro–wind power systems has become an extremely effective way to create safe and efficient systems. This paper aims to study the optimal coordinated operation of a hybrid power system based on a newly established Simulink model. The analysis of the optimal coordinated operation undergoes two simulation steps, including the optimization of the complementary mode and the optimization of capacity allocation. The method of multiple complementary indicators is adopted to enable the optimization analysis. The results from the complementary analysis show that the hydraulic tracing effect obviously mitigates operational risks and reduces power losses under adverse wind speeds. The results from the analysis of capacity allocation also show that the marginal permeation of installed wind capacity will not exceed 250 MW for a 100 MW hydropower plant under random wind speeds. These simulation results are obtained based on the consideration of some real application scenarios, which help power plants to make the optimal operation plan with a high efficiency of wind energy and high hydro flexibility.

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.

Optimal allocation of wind power hybrid energy storage capacity based on ant colony optimization algorithm

Renewable energy sources, such as wind power, face challenges owing to their erratic construction and intermittent nature, leading to the emergence of energy storage strategies to achieve grid stability and reliable electricity supplies. Determination of the correct size of energy storage devices for wind power plants is complicated. In this study, the ant colony optimization (ACO) algorithm is proposed for the best distribution/sizing of wind-generated hybrid storage capacity. Ants’ foraging habits motivate the development of wind turbines with high dependability, high efficiency and low energy storage costs. A study is conducted to confirm the practicality of the proposed method, using real data from a wind power plant to calculate the contrast between wind energy production and load consumption features, to influence the appropriate quantity for energy storage organization. The study demonstrates that the ACO approach provides precise and efficient solutions for determining the optimal energy distribution in wind power plants.

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.

AI for AI-based intrusion detection as a service: Reinforcement learning to configure models, tasks, and capacities

Intrusion Detection Systems (IDS) increasingly leverage machine learning (ML) to enhance the detection of zero-day attacks. As operational complexities increase, enterprises are turning to Intrusion Detection as a Service (IDaS), requiring advanced solutions for efficient ML model selection and resource allocation. Existing research often focuses primarily on accuracy and computational efficiency, leaving a gap in solutions that can dynamically adapt. This study introduces a novel integrated solution, Auto-IDaS, which employs advanced Reinforcement Learning (RL) techniques for real-time, adaptive management of IDS. Auto-IDaS uses the Deep Q-Network (DQN) algorithm for dynamic ML model selection, automatically adjusting configurations of IDaS in response to fluctuating network traffic conditions. Simultaneously, it utilizes the Twin Delayed Deep Deterministic (TD3) algorithm for optimizing capacity allocation, aiming to minimize computational costs while maintaining service quality. This dual approach is innovative in its use of RL to address both selection and allocation challenges within IDaS frameworks. The effectiveness of TD3 is compared against Simulated Annealing (SA), a traditional optimization technique. The results demonstrate that utilizing DQN to dynamically select the model significantly improves the reward by 0.29% to 27.04%, effectively balancing detection performance (F1 score), detection time, and computation cost. Regarding capacity allocation, TD3 accelerates decision times approximately 5×106 times faster than SA while retaining decision quality within a 10% range comparable to SA’s performance.

Taking a Portfolio approach to wind and solar deployment: The case of the National Electricity Market in Australia

We formulate a new, computationally inexpensive framework based on Mean–Variance Portfolio Theory to determine the optimal allocation of wind and solar capacity, which can leverage the complementarity between intermittent solar and wind generation to meet electricity demand at the lowest cost, with gas acting as reserve generation in times of insufficient wind and solar output, and the variance of the mismatch between demand and the output of wind and solar is taken as the risk indicator. We then apply this framework to the context of the National Electricity Market in Australia. Our result reveals that, assuming sufficient network capacity, the estimated lowest generation cost achievable is $45.26/MWh, where the expected percentage of unserved energy (USE) is 0.0044%. For a risk-averse planner, a level of USE less than 0.0001% may be achieved at the expense of almost doubling the cost to $88.23/MWh. Under the current network capacity constraints, we estimate these costs to increase to $53.09/MWh and $89.90/MWh respectively, while the USE could rise to 0.017%. Moreover, network expansions in Queensland are found to be the most beneficial. On the other hand, a 4-hour battery storage system with power capacity equivalent to 20% of the peak load could help reduce the cost by up to 6.8% alongside further decrease in the USE. Our results are also shown to differ drastically from the conventional, output-based approach. Since the two approaches address two different questions depending on whether demand is flexible, demand flexibility is also an important factor.

Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm

AbstractHow to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh.

Optimal pricing decision and capacity allocation of opaque selling in airline revenue management

Optimal pricing decision and capacity allocation of opaque selling in airline revenue management

Research on the optimal capacity configuration of green storage microgrid based on the improved sparrow search algorithm

Green storage plays a key role in modern logistics and is committed to minimizing the environmental impact. To promote the transformation of traditional storage to green storage, research on the capacity allocation of wind-solar-storage microgrids for green storage is proposed. Firstly, this paper proposes a microgrid capacity configuration model, and secondly takes the shortest payback period as the objective function, and uses the improved sparrow search algorithm (ISSA) for optimization. Firstly, the Logistic-Tent compound chaotic mapping method is added to the population initialization of the sparrow search algorithm (SSA). Secondly, the adaptive t-distribution mutation is used to improve the discoverer, and the overall optimization ability of the algorithm is improved. Finally, the hybrid decreasing strategy is adopted in the process of vigilance position update. The ISSA can improve the search efficiency of the algorithm, avoid premature convergence and enhance the robustness of the algorithm, which is helpful to better apply to the optimal configuration of wind-solar-storage microgrid capacity in green storage. By analyzing the optimal capacity allocation results of two typical days, the system can better adapt to the dynamic storage requirements and improve the flexibility and sustainability of the supply chain.

Capacity optimization of independent hybrid renewable energy system under different operational strategies based on improved gray wolf algorithm

Renewable energy sources such as wind and solar power exhibit strong stochasticity and volatility, resulting in decreased power supply security and sustainability. A strategically optimized hybrid renewable energy system (HRES) is crucial for maintaining stable load operations and achieving sustainable energy development. This paper introduces an energy optimization management model for an independent HRES consisting of wind turbines, photovoltaic systems, diesel generators, and energy storage units. Operational strategies focus on energy storage-led loads following diesel generator-led load prioritizations. The model aims to optimize objectives to include economic, environmental, and power supply reliability indices. A dynamic adaptive parameter approach balances the parameters of the objective function at various instances. The optimal capacity allocation of the model is solved using the improved gray wolf optimization (IGWO) algorithm. This approach incorporates the golden sine strategy, the levy flight strategy, and the dynamic inverse learning strategy into the traditional GWO algorithm. Analyzing different test functions, evaluation metrics, and actual load data indicates that the proposed algorithm excels in global optimization capabilities and search speeds. The model significantly reduces the economic and environmental costs of the HRES microgrids and improves the sustainable development of renewable energy in various scenarios.

Optimized Battery Capacity Allocation Method for Wind Farms with Dual Operating Conditions

In order to solve the problems of wind power output volatility and wind power participation in frequency regulation, a method for optimizing the capacity allocation of wind farm storage batteries based on the dual grouping strategy and considering the simultaneous execution of the dual conditions of energy storage in fluctuation smoothing and primary frequency regulation is proposed. Firstly, a two-layer model is established to optimize the capacity allocation under dual operating conditions, i.e., the planning layer takes into account the lifetime, cost, and benefit, and the operation layer considers the wind turbine reserve backup and storage control to participate in the primary frequency regulation in a cooperative manner. Then, the dual battery pack operation strategy is embedded with the variational modal decomposition method to determine the charging and discharging operation strategy of energy storage after considering the grid-optimized reference power. An improved particle swarm algorithm with inverse learning pre-optimization combined with variational crossover post-optimization is embedded in the GUROBI computation to obtain the optimal battery storage capacity allocation scheme. Finally, the superiority of the model proposed in this paper in terms of improving energy storage utilization, service life, and economic efficiency as well as reducing wind power load shedding is verified by comparing it with a single execution working condition scenario and traditional battery control strategy.

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.

Multi‐factor Analysis of Capacity Allocation Optimization for Novel Wind‐Hydrogen Coupled Power Generation Systems

Wind power generation has the problem of wind resource waste. Wind‐hydrogen coupled can enhance wind power's utilization and revenue. Currently, there is no discussion of the impact of multiple factors on the system configuration. In this study, take the annual profit of the wind‐hydrogen coupled power generation systems (WHCPGS) as the objective function, and construct the multi‐factor capacity configuration model. Using particle swarm optimization (PSO) to solve the model, and analyzes the impact of multi‐factors on the system's annual profit and revenue mode. The results show that with 30 wind turbines, the coupled system annual profit increases by 43% compared to the alone system. The higher investment does not make the system more rewarding. The configuration results are the same for investments of 0.8 billion ¥ and 1 billion ¥. The price at 0.20 ¥ kW−1 h−1, the coupled system has the optimal annual profit. Fuel cells’ (FCs) electricity price and hydrogen transportation distance are crucial factors influencing the mode of the system profit. This study has specific reference significance for the practical application of capacity configuration of the system.