With the rapid growth of renewable energy installation capacity, the law for flexibility of demand changes, and the economy of flexible resources improve alongside technology, as in electrochemical energy storage. The method of balancing the evolution of flexibility requirements and development of energy storage technology during planning, is crucial in improving system flexibility and economy. Therefore, this study conducts research on the dynamic planning method of flexible resources, considering the evolution law on flexibility of demand. First, the evolutionary relationship between renewable energy penetration growth and flexible supply and demand is analyzed, and the principle and influencing factors of multi-stage flexible resource planning are revealed. Then, considering the evolution law on flexibility of demand and the continuous improvements in energy storage technology economy, a multi-stage flexible resource optimization allocation model is constructed to offer a solution. Finally, the feasibility and effectiveness of the multi-stage flexible resource optimization allocation method are verified by example, considering the continuous improvement of energy storage technology economy and the gradual withdrawal of thermal power units, which provides guidance for the coordinated development of wind power in the future.
The volatility and unpredictability of power systems in the presence of a high proportion of renewable energy access have become pronounced, highlighting the contradiction between power abandonment and insufficient power supply capacity. Furthermore, collaborative planning of source-load-storage flexible resources has emerged as key to ensuring the reliability of power supply and effective integration of renewable energy. Based on the definition of the generalized spinning reserve, this study reveals the operation simulation mechanism of the new power system from the perspective of considering the dual objectives of power supply and renewable energy accommodation, on which a source-load-storage flexible resource optimization planning model integrating investment decisions and annual 8 760 h timing operation simulation is established. Finally, a simulation analysis is conducted based on provincial power grid data with a high proportion of new energy access. The findings indicate that, as the permeability of new energy increases, both the system power abandonment and power deficit rates correspondingly increase. They also reveal that the flexible resources of source load storage can effectively reduce the power deficit and power abandonment rates. Moreover, the flexible transformation of thermal power, the demand responses of valley filling and energy storage (charging) are highly sensitive to “power abandonment,” and the demand responses of peak cutting and energy storage (discharge) have high sensitivity to the “power deficit.”
Building a renewable type of power system that adapts to the increasing proportion of renewable energy requires a multi time scale energy storage system as support. The key to energy transformation driven by the “dual carbon” goal lies in quantifying the carbon reduction effect brought by renewable energy and energy storage investment. Its essence is the substitution of zero carbon energy such as renewable energy for traditional energy with different carbon emission intensities. Due to differences in resource endowment, power structure, and load characteristics in different regions, the carbon reduction effect and collaborative environmental value brought by renewable energy and energy storage investment also have significant differences. Therefore, this article focuses on the joint planning method of renewable energy and multi time scale energy storage that takes into account the synergistic environmental value. A framework for joint planning of renewable energy and energy storage that takes into account the synergistic environmental value is constructed, and the synergistic environmental value in joint planning of renewable energy and energy storage is elaborated from three aspects: operational characteristics, regional heterogeneity, and market mechanism, A joint planning model for renewable energy and multi time scale energy storage has been proposed to adapt to diversified low-carbon policies. Finally, based on the actual system data validation, the effectiveness of the model in this paper is verified. The example results show that low-carbon policies and regional heterogeneity will significantly affect the value of the collaborative environment of renewable energy and energy storage investment decisions at different time scales.
Existing research related to wind-farm energy-storage allocation methods has failed to consider power market trading scenarios and the calculation of energy-storage charging and discharging costs. Therefore, a storage-capacity allocation method for wind-farm day-ahead and intraday trading that considers multiple transactions and costs of storage charging and discharging is proposed. First, intraday electricity price history data are clustered using the Canopy-K-means clustering method to determine the day-trading scenario. Second, with reference to the Nordic electricity market trading model, we propose a single charge/discharge cost discounting method for energy storage and a storage-allocation method for wind farms that participate in day-ahead and intraday markets while considering multiple scenarios. Finally, based on a wind farm in Denmark as an example, results show that the annual net revenue of the proposed energy-storage configuration method can be increased by as much as 6.92% and 5.11% as compared with those under single-scene and no energy-storage configurations, respectively. These results verify the feasibility and effectiveness of the proposed method and provide a valuable reference for the wind-farm energy-storage configuration method.
Shared energy storage equipment on the residential user side can effectively reduce the cost of energy storage and increase the benefits of energy storage equipment. To solve the problem of capacity redundancy in the shared energy storage configuration on the residential user side, this study proposes an optimal method for residential shared energy storage that considers capacity reduction. First, shared energy storage is preliminarily optimized to maximize the net income of all residents in the entire life cycle of energy storage, and the initial charging and discharging strategies of residential users are obtained. Second, the probability of simultaneous charging and discharging for residential users is calculated by analyzing the charging and discharging strategies using statistical principles. Third, a reduction factor is introduced to modify the preliminary allocation results of the shared energy storage. By constraining the simultaneous charging and discharging probabilities of residential users, the minimum allocation cost of shared energy storage, considering the risk cost, is taken as the objective to obtain the optimal reduction factor. Finally, the simulation reveals that the proposed shared energy storage system can further reduce the scale and cost of shared energy storage compared to traditional shared energy storage.
Under the dual carbon background, a key task of the planning department of the local power supply company is to carry out energy storage planning in conjunction with distributed photovoltaic planning. This paper presents a practical model and solution method for energy storage location and capacity determination for distributed photovoltaic consumption in medium-voltage distribution networks. First, production simulation is used to analyze the impact of distributed photovoltaic grid connection on the distribution network. Second, a scenario reduction method is proposed based on a robust idea, whereby the scenario with the most serious impact on distributed photovoltaic consumption in a distribution network is selected. Finally, a linear optimization model is proposed for the location and capacity of the medium-voltage distribution network. With the goal of minimizing the maximum charging and discharging power (electricity) per hour of energy storage, the line overload and out-of-limit node voltage constraints are expressed as a linear function of the charging and discharging power of energy storage, using the sensitivity coefficient method. The case study of the improved IEEE33 bus system shows that the solution method proposed in this paper reduces the complexity of the problem by simplifying the two-layer optimization problem into a single-layer, thereby reducing the number of constraints by narrowing the scenarios for linear optimization to quickly and effectively determine the location and capacity of energy storage.
Increasing the permeability of distributed energy results in a greater number of complex problems related to voltage quality, safety, and stability in distribution networks. Achieving a single optimal configuration of reactive power or energy storage to satisfy the increasingly diverse requirements of modern power grids is difficult. In this study, a configuration strategy that combines energy storage and reactive power is proposed to meet the requirements of new energy distribution networks in both active-power regulation and reactive-power compensation. The strategy also realizes a balanced optimization of flexibility, voltage quality, and economy, and adapts to the influence of new energy under different permeabilities. First, a safety and stability evaluation system of the distribution network is established based on flexibility and reactive-power demands. Second, based on the coupling between the planning and operating layers, a two-layer model of optimal energy-reactive power allocation in the distribution network is established. A small-probability random mutation method is then used to improve the standard particle swarm optimization algorithm to reduce the possibility of falling into local optima. Finally, an improved IEEE-33 node distribution system is used in a simulation test to verify the rationality and effectiveness of the scheme. Simulation results show that the proposed scheme can effectively improve the economy, security, and stability of the distribution network, and a multi-objective optimal configuration scheme can be obtained under new energy permeabilities.
With large-scale and decentralized access to distributed generation sourced from distribution networks, meeting the needs of widely perceived, flexible, controllable, and coordinated operations under a centralized distribution network operational control mode is difficult. Cluster control based on “intra-cluster autonomy and inter-cluster coordination” can reduce the difficulty of distribution network operational control, and cluster partition is critical in achieving decentralized optimal operation of the distribution network. Therefore, a distribution network cluster partitioning method that considers source-load-storage matching is proposed. Based on the power complementarity characteristics between nodes in a cluster and combined with a modular index based on electrical distance, a comprehensive performance index that considers the cluster structure and matching of source-load-storage resources is established to achieve cluster partitioning. To address the uncertainty of wind power and photovoltaic output, the deviation of the renewable energy output from the predicted value is considered as a disturbance state. A two-stage probabilistic optimal dispatching model for a distribution network is constructed, and the synchronous alternating direction method of multipliers (SADMM) is used for optimal cluster dispatching. Finally, an improved IEEE 33-node system is used as an example to verify the effectiveness and feasibility of the proposed distribution network cluster partitioning method and decentralized optimal operational strategy.
Advanced adiabatic compressed air energy storage (AA-CAES) devices are increasingly attracting attention for their advantages in offering large capacity, long life, less pollution, and low cost. The characteristics of large capacity and multiple energy co-supply are matched with those of building energy consumption, and AA-CAES is connected to the regional comprehensive energy system comprising a multi-building micro-grid. In this study, a regional integrated energy system (RIES) model consisting of multiple building microgrids with AA-CAES is established. The seasonal efficiency model is established to optimize the operation mode of AA-CAES seasonally by considering the dual energy storage state of compressed air energy storage equipment and analyzing the multi-energy flow balance constraints within the region. On this basis, the quadratic constraint problem for the optimal comprehensive benefit of the system is constructed and solved by the Gurobi solver. The simulations show that the improved AA-CAES can coordinate the multi-energy optimization of multiple building microgrids in the region, improve the economy, increase energy savings and environmental protection of the system, and promote the local consumption of renewable energy in energy cascade utilization.
Limited by the individual capacity of the electrolyzer, a hydrogen production plant requires multiple electrolyzers to form a cluster and meet the scale requirements of the integrated photovoltaic hydrogen project. Considering the startup/shutdown and ramping inertia of the electrolyzers, it is necessary to propose a scheduling method for the electrolyzer cluster system. The proposed method dynamically adjusts the startup/shutdown states and power allocation to adapt to the temporally correlated uncertainty of photovoltaic power. First, the continuous-time stochastic process of photovoltaic power is modeled based on Itô’s theory, whereby, a stochastic optimization model is constructed for the scheduling of the electrolyzer cluster. Subsequently, by utilizing trajectory sensitivity decomposition based on the stochastic differential equations, the stochastic optimization is transformed into a deterministic one. Finally, a rolling-horizon optimization scheme of the startup/shutdown commands is proposed for the affine control law of power allocation. The case studies based on a demonstration project under construction in Inner Mongolia show that, compared to deterministic optimization, the proposed approach improves the utilization of photovoltaic power, increasing the benefits from hydrogen production.
To promote the absorption of new energy, investigating multi-time-scale operational optimization of distributed electro-hydrogen coupling systems is essential. First, the characteristics of electric, hydrogen, and thermal energy systems are analyzed in terms of uncertainty and response characteristics. A multi-time-scale operational optimization model of day-ahead, day-in, and real-time is then constructed. Deep reinforcement learning is then used to solve the optimization model. Finally, an example of a distributed electro-hydrogen coupling system in a region is analyzed. The results of the example verify not only the effectiveness of deep reinforcement learning but also the effects of the electro-hydrogen coupling system on new energy consumption.
With the large-scale application of frequency modulation in power systems and the joint participation of conventional thermal power and storage systems, the output control strategy of storage and thermal power units has become a key issue in secondary frequency modulation. Using the two-stage SOC real-time prediction method, this study actively predicts the charging status of the frequency regulation system after it completed tracking the AGC command. Based on the predicted data and AGC directives, joint feed-forward control directives that consider both the system frequency modulation capability and storage SOC state are obtained. The output targets of the thermal power unit and storage system in different states are determined, and complementary and coordinated operation of the storage and tracking of the AGC directives by the thermal power unit is achieved. The energy storage unit balancing strategy based on the charging state is introduced in the energy storage system to optimize the SOC consistency of the energy storage system and enhance the overall operation depth of the energy storage system. A typical joint frequency modulation scenario is built in MATLAB/Simulink to simulate and validate the proposed control strategy. The results reveal that the proposed control strategy offers significant advantages in tracking AGC commands, improving the comprehensive frequency modulation index of the system, and improving the state of charge of the energy storage monomer.
In recent years, the energy savings and loss reduction of power grids have been widely studied. As flexible regulated resources gain large-scale access to distribution networks, exerting the loss reduction potential of flexibly regulated resources by proposing a mobile energy storage device (MESD) and a network reconfiguration collaborative optimization strategy for loss reduction scenarios becomes necessary. The collaborative optimization strategy is divided into two stages. In the first stage, the source-load uncertainty is characterized by the scenario analysis method, and the network reconfiguration model is established with the minimum network loss as the objective function to obtain the network reconfiguration scheme. Owing to the large number of distribution network nodes, a network loss sensitivity analysis method is proposed to narrow the search range in order to enhance the efficiency of the solution, and the above reconfiguration scheme is combined with it to pre-screen the charging/discharging node set for MESD. In the second stage, the objective is to minimize the network loss of the distribution network and the traffic cost of mobile energy storage by considering the connection/operation state constraint, charging/discharging power or capacity constraint of the MESD, and the power balance and power flow safety constraint of the power network. A charging/discharging dispatching model of traffic network-power network convergence applicable to the MESD is constructed, and the CPLEX solver is invoked to solve the traffic planning and charging/discharging power dispatching plan of the MESD. Finally, a simulation analysis is performed using the IEEE 33-bus distribution system. The simulation results reveal that the active network loss of the system is reduced by 552.17 kWh, and the loss reduction range is reduced by 31.9%, verifying the effectiveness of the proposed strategy.
Energy-storage technology is currently under rapid development. Accordingly, to ensure the safety and stability of wind-power grid connections, the use of hybrid energy-storage systems (HESSs) for wind-power leveling at wind-power grid connections has become a mainstream wind-power leveling method. However, most leveling strategies have a high lag in leveling power. This strategy combines grey prediction and the adaptive Savizkg-Golag (SG) algorithm to obtain the grid-connected reference and output reference powers of the wind power and HESS, respectively. According to the energy-storage characteristics of lithium batteries and supercapacitors, the output reference power can be decomposed by the variational mode decomposition (VMD) algorithm to achieve a reasonable distribution of the HESS output power. Finally, an analysis verifies that the strategy has a good wind-power smoothing effect and can solve the problem of hysteresis in the traditional smoothing strategy to reduce fluctuations in the state-of-charge (SOC) curve of the HESS, thereby reducing its capacity allocation requirements.
The deployment of energy storage system (ESS) in wind farms is currently an effective means to improve the accuracy of wind farm response scheduling. In order to improve the dispatchability of wind farms and wind power consumption while extending the service life of ESS, a wind storage co-generation system tracking wind power planned output control strategy that takes into account wind power consumption during valley hours is proposed. Firstly, the charging and discharging intervals of ESS within 1 day are divided based on the time series relationship between wind power characteristics and load demand. Secondly, according to the division of charging and discharging interval segments, an ESS control strategy is proposed with interval control, in which the upward deviation between the actual wind power and the planned output limit is corrected as the target storage power in the charging interval, and the downward deviation between the actual wind power and the planned output limit is corrected as the target release power in the discharging interval. Finally, a control strategy is proposed to modify the ESS output using fuzzy control, taking into account the current state of charge (SOC) and the peak-to-valley tariff. Simulation results show that the proposed control strategy improves the ability to track the scheduling plan while extending the life of the ESS, and also provides good “peak-to-valley arbitrage” benefits.
