Battery Energy Storage Systems Degradation Research Articles

Optimized energy management of a solar battery microgrid: An economic approach towards voltage stability

As the adoption of renewable energy sources (RESs) continues to surge, and the concept of microgrids (MGs) gains widespread recognition, the need for efficient battery energy storage system (BESS) management within MGs has become increasingly prominent. This paper introduces a novel Energy Management System (EMS) designed for grid-connected MGs, with the primary objectives of enhancing the energy economics of the MG, minimizing BESS degradation, and ensuring that the point of common coupling (PCC) voltage remains within prescribed limits. The proposed EMS utilizes a finite-horizon Model Predictive Control (MPC) framework to seamlessly coordinate BESS operations, encompassing charging, discharging, and reactive power provision. This coordination is done in conjunction with solar power generation, load demand, electricity pricing, and feed-in tariffs. Furthermore, the EMS accounts for uncertainties related to load and solar demand using a Monte Carlo simulation approach. The optimization goal of the proposed EMS is to minimize the expected total operational cost of the MG over a 24-hour prediction horizon. This cost encompasses both economic costs and battery degradation costs. The optimization process adheres to a set of constraints that consider BESS limitations, such as state of charge (SoC) constraints and charging/discharging limits, as well as inverter capacity limits and PCC voltage requirements. Conducting rigorous case studies on the MATLAB Simscape Electrical platform and validating them in real-time using the OPAL-RT simulator demonstrate the effectiveness of the proposed EMS across diverse operational scenarios and pricing models. The findings of these case studies provide valuable insights into the effectiveness of the EMS in real-world MG applications.

Stochastic optimal allocation for a battery energy storage system in high renewable-penetrated distribution networks

As the penetration of renewable distributed generation (RDG) continues to grow, the stochastic and intermittent nature of its output imposes significant challenges on distribution networks (DNs), such as source–load mismatch and voltage fluctuations, which seriously affects the safety and reliability of the system. Thus, this paper presents a stochastic optimal allocation method for a battery energy storage system (BESS) in the DN, with the consideration of annual load growth, BESS degradation, and DN operation, aiming to minimize the overall cost of DNs and harvest more renewable energy. Based on the rainflow-counting concept, BESS degradation is efficiently modeled and linearized to improve solvability. Additionally, to address the uncertainties of RDG outputs and loads, a stochastic optimization (SO) method is adopted. Furthermore, considering that a large number of integer variables of the BESS allocation model may cause a heavy computational burden, a feasibility pump-based solution algorithm is introduced to accelerate the solving speed. Finally, the effectiveness of the proposed BESS allocation method and the solution algorithm is verified on a 33-bus DN system through comparative analyses, showing high efficiency and performance.

An effective strategy for unit commitment of microgrid power systems integrated with renewable energy sources including effects of battery degradation and uncertainties.

The large use of renewable sources and plug-in electric vehicles (PEVs) would play a critical part in achieving a low-carbon energy source and reducing greenhouse gas emissions, which are the primary cause of global warming. On the other hand, predicting the instability and intermittent nature of wind and solar power output poses significant challenges. To reduce the unpredictable and random nature of renewable microgrids (MGs) and additional unreliable energy sources, a battery energy storage system (BESS) is connected to an MG system. The uncoordinated charging of PEVs offers further hurdles to the unit commitment (UC) required in contemporary MG management. The UC problem is an exceptionally difficult optimization problem due to the mixed-integer structure, large scale, and nonlinearity. It is further complicated by the multiple uncertainties associated with renewable sources, PEV charging and discharging, and electricity market pricing, in addition to the BESS degradation factor. Therefore, in this study, a new variant of mixed-integer particle swarm optimizer is introduced as a reliable optimization framework to handle the UC problem. This study considerssix various case studies of UC problems, including uncertainties and battery degradation to validate the reliability and robustness of the proposed algorithm. Out of which, two case studies defined as a multiobjective problem, and it has been transformed into a single-objective model using different weight factors. The simulation findings demonstrate that the proposed approach and improved methodology for the UC problem are effectivethan itspeers. Based on the average results, the economic consequences of numerous scenarios are thoroughly examined and contrasted, and some significant conclusions are presented.

A useable multi-level BESSs sizing model for low-level data accessibility with risk assessment application under marketization and high uncertainties

The applications and advantages of Battery Energy Storage Systems (BESSs) are noticeable. However, the financial aspects of BESS still require further clarifications for investors to encourage them to size BESS more than before. The uncertain prices, the impact of installed capacity of generation/demand on profit, income sources, and the degradation of BESS are the primary factors that make the financial analysis of BESS more challenging. Additionally, investors' ability to develop an accurate sizing model is hampered by low-level data accessibility. This paper presents a multi-level sizing model for investors using available public data. The sizing problem and operational constraints of BESS have been modeled at the upper levels, while the lower levels present the capacity, day-ahead, real-time, and reserve markets. The uncertainties of prices and capacities are also considered with the risk assessment application using the Conditional Value at Risk (CVAR) index. Furthermore, a new linear degradation and replacement model is developed using a novel linear recursive function to capture the impact of BESS degradation. Implementations revealed that considering various markets can improve the net present value (NPV) at least by 5.31%. Besides, the proposed degradation and replacement model can improve the NPV and rate of return (ROR) by 6.64% and 33.65%, respectively.

Curtailment to cashflow: Exploring BESS and hydrogen for renewable energy profitability

The sporadic nature of renewable energy sources has led to increased energy curtailment. Hence, the necessity to efficiently utilize curtailed renewable energy using Battery Energy Storage Systems (BESS) and hydrogen technology is paramount. This study explores curtailed renewable energy (wind and solar) utilization through three cases: Case 1 - BESS, Case 2 - alkaline water electrolyzer (AWE) for hydrogen energy, and Case 3 - a hybrid system (BESS and AWE). Hourly data for California in 2020 is used. Dynamic optimization tackles the mixed-integer linear programming (MILP) problem, aiming to maximize profits across scenarios. Factors include power source (wind, solar, or hybrid), energy storage (BESS, hydrogen, or hybrid), and curtailed power availability (25 % to 100 %). Results show that 100 % solar curtailment availability in Case 2 yields highest profits and rapid payback in California. Cases 1 and 3 underperform due to high BESS capital, replacement, and degradation costs. The study, catering to operators and investors, explores curtailed energy dimensions for profit. Comprehensive cost considerations, encompassing BESS degradation, operational, and variable costs, ensure precise estimates. Sensitivity analysis underscores the crucial role of technology costs, suggesting avenues for expense reduction. The study provides guidance for effective curtailed energy repurposing, considering evolving technology costs.

Optimal Sizing of Battery Energy Storage Systems Considering Degradation Effect for Operating and Electricity Cost Minimization in Microgrids

A microgrid is a low‐voltage distribution network designed to provide power for small‐scale and isolated communities consisting of distributed generation and energy storage systems. In order to achieve reliable power and proper energy utilization, energy storage systems plays a vital role in microgrids by storing energy during off peak hours and discharges energy during peak hours. One of the major issues in the isolated microgrids with intermittent nature of distributed generations is the balance of energy demand. This can be achieved by appending renewable energy sources with suitable battery energy storage systems (BESS), to provide the reserve support in meeting the load demand. Battery degradation effect plays a major role in analyzing the performance of BESS lifetime. Battery degradation effect relates the capacity reduction of energy of BESS that is to be delivered to meet the load demand. Therefore, microgrid systems with BESS considering degradation effect should be optimized in such a way as to obtain minimum operating cost while ensuring minimum electricity cost to customers. Simulations of hourly battery discharges rates and simulation of actual discharge rates with obtained simulated rates need to be calculated to determine the degradation effect. The battery degradation cost and lifetime can be calculated correspondingly to minimize the objectives. Correspondingly, herein, an optimization algorithm for microgrid operating cost and customer electricity cost minimization for 24 h time horizon while considering BESS degradation effect by determining kWh and MWh throughput is presented. Particle swarm optimization (PSO), accelerated particle swarm optimization (APSO), Jaya optimization (JAYA) technique, and linear programming interior point algorithm (LP‐IP) are applied to determine the optimal operating cost and electricity cost by simulating BESS degradation parameters. Results obtained using PSO, APSO, JAYA are compared with LP‐IP solver‐based algorithm for BESS lifetime, BESS degradation cost, microgrid operating cost, and customer electricity cost for 24 h.

Model-free data-driven approach assisted Deep Reinforcement Learning for Optimal Energy Management in MicroGrid

Uncertainties of solar PhotoVoltaic (solar PV) generation and Electric Vehicle (EV) demand are major issues for Optimal Energy Management (OEM) tasks in MicroGrid (MG), especially regarding power system stability and increased overall demand. A Probabilistic Load Flow (PLF) with a Battery Energy Storage System (BESS) controlled by an Energy Management System (EMS) can deal with these issues. However, the PLF and optimization algorithm based on the iterative method leads to a high computation burden. Therefore, this paper proposes a model-free data-driven approach assisted Deep Reinforcement Learning (DRL) to decrease the computation burden of PLF and problem-solving. Deep Neural Networks (DNNs) are developed as a model-free data-driven to estimate the power flow parameters of MG instead of PLF. Moreover, the DRL named a Deep Deterministic Policy Gradient (DDPG) is deployed as the optimization algorithm to find the optimal solution in the OEM task. In addition, finding appropriate parameters of the DDPG is proposed in this paper. To showcase the efficacy of the proposed method, a low-voltage distribution network is developed as the MG. Objective functions including exchanged energy, carbon emission, and BESS degradation costs are considered in this work. Simulation results indicate that the proposed method is capable of reducing the computation burden by 88.47% in comparison to the Differential Evolution (DE) algorithm that uses 10 populations. Moreover, the best agent model obtained from finding suitable parameters of DDPG can provide a total cost of 115.63 USD/day, which is the lowest cost compared with the cost obtained by the DE.

Reliability Framework Integrating Grid Scale BESS Considering BESS Degradation

To ensure the future networks’ secure and flexible operation, NOs will increasingly rely upon grid-scale storage services as a secondary (ancillary) service. During critical contingencies, NOs may drive some battery energy storage systems (BESS) that operate at higher depth-of-discharge cycles for increasing reliability benefits. This eventually could lead to accelerated degradation of BESS. The reliability benefits versus the accelerated BESS degradation risks over the BESS lifetime are not deeply investigated in the up-to-date literature. This paper develops a reliability evaluation framework integrating a Bi-Level Sequential Monte Carlo loops with a detailed BESS degradation model. The integration captures the BESS normal and accelerated cycle and capacity degradation across a multi-year network analysis. Annualized indices are defined for assessing the risks of BESS accelerated degradation named as expected equivalent life cycle accelerated degradation (EELCAD), expected equivalent capacity degradation (EECD) and expected battery accelerated degradation costs (EBADC). In addition, it provides a techno-economic assessment of reliability benefits versus the degradation risks over the battery’s lifetime. Therefore, the paper presents an in-depth analysis of the benefits and risks associated with battery degradation and the potential implications in long-term planning studies.

Data-driven model predictive control of community batteries for voltage regulation in power grids subject to EV charging

In this paper, a Model Predictive Control (MPC) for community Battery Energy Storage Systems (BESS) is proposed to mitigate the Electric Vehicle (EV) charging demand while maintaining voltage regulation in residential power grids. With the increased penetration of renewable-based Distributed Energy Resources (DERs) and the EV uptake, distribution grids are highly subject to voltage fluctuations due to back power feed from DERs or significant EV charging demand. Conventionally, Distribution Network Operators (DNOs) have addressed this issue by adjusting the taps of transformers. However, managing the naturally unpredictable renewable generation and the power consumption behaviour of technology owners requires advanced real-time algorithms. In this paper, we apply a real-time control algorithm, based on Model Predictive Control (MPC), to a community BESS in order to reduce voltage fluctuations. The MPC algorithm performs in real-time based on readings from smart meters of households. It predicts the future values of voltages at households based on the current and past readings of smart meters, and calculates the BESS charging or discharging rate such that future voltage deviations are minimised. Simulations on the real-world power consumption data from Victorian residential consumers, in Australia, show that the proposed controller performs significantly better than the fixed charging/discharging BESS schedule in both voltage regulation and BESS degradation.

Accelerating Efficient Installation and Optimization of Battery Energy Storage System Operations Onboard Vessels

Emerging large battery energy storage systems (BESSs) are key enablers in the electrification of the shipping sector. With huge government investments in BESSs, there are large gaps between the government supported BESS initiatives and actual BESS integration results on vessels. This study aims to close these gaps, allowing BESSs to become the preferred solution for ship owners without needing government support. Firstly, this industry-driven study reviews both the industrial approaches to achieve CO2 emission reductions and the fuel savings and emission reductions from 500 BESS installations on various vessels. Secondly, a 630 kWh BESS retrofitted onto a hybrid-electric vessel is used to quantitively identify the improvement requirements for installations and operations. The installations required many custom designs that were expensive and have high failure risks. The standardization of interfaces’ between BESSs and vessels is thus urgently required. The BESS was intended for spinning reserve capacity and peak shaving but in practice was under-used in terms of energy throughput (shallow cycles and low equivalent full cycles of 80 versus the design specification of 480 yearly). Thirdly, this study develops new, integrated BESS operational models by learning from large operational data, balancing BESS degradation against fuel saving and utilizing onshore/offshore green power supply/charging. The R&D of BESS is required to deal with the increasing safety requirements and further CO2 emission reductions. Finally, four BESS acceleration scenarios were established to facilitate the technical and operational transferability through utilizing digitalization.

Principle component analysis-based optimized feature extraction merged with nonlinear regression model for improved state-of-health prediction

The state-of-health (SOH) estimation and prediction is critical for battery energy storage systems (BESS) to detect poor battery performance. The BESS consists of a high-energy battery pack with series and parallel connections. Unlike unit cells, battery packs with series and parallel combinations must account for cell-to-cell imbalance. The cell-to-cell imbalance indicates deviations in voltage, state-of-charge (SOC), and temperature. A serious imbalance in battery packs can hamper their capacity, power, and efficiency. However, conventional methods of SOH estimation and prediction only consider one or two degradation features. To improve the SOH prediction performance of a battery pack, more features than those used for a unit cell must be considered. In this study, an optimal regression model is used to propose a feature extraction method for reflecting new degradation features. Feature extraction based on principal component analysis takes into account various degradation features. The proposed method can highlight various BESS degradation features and improve SOH prediction performance through combination of the principal component analysis and optimal regression model.

Affine-arithmetic-based microgrid interval optimization considering uncertainty and battery energy storage system degradation

Microgrids can effectively integrate renewable energy sources (RESs) and provide power for local customers. However, uncertainties of RESs and loads pose challenges to microgrid operation. The traditional point optimization method is unrealistic, and the widely used stochastic optimization (SO) method is time-consuming. Besides, battery energy storage systems (BESSs) are critical dispatchable devices to alleviate adverse effects of uncertainty, so an accurate nonlinear degradation cost model of BESSs should also be proposed. To handle such problems, the paper proposes an affine–arithmetic (AA)-based microgrid interval optimization (IO) method considering uncertainty and BESS degradation. First, the AA theory is introduced to model the RES and load variation ranges as intervals and calculate the interval uncertainty. Then, a nonlinear BESS degradation cost model is proposed, which can assess battery degradation costs considering different charging and discharging behaviors. The nondominated sorting genetic algorithm-II (NSGA-II) is employed to solve the proposed microgrid IO framework. For validation, the proposed IO method was compared with the point optimization method and SO method under various uncertainty realizations in a modified IEEE 33 bus system. The simulation results indicated the effectiveness of the proposed IO method in terms of an equilibrium between the simulation time and optimization performance.

Single electricity market forecasting and energy arbitrage maximization framework

The rapid deployment of renewable-based generation to meet the net-zero carbon targets has affected the wholesale energy paradigm. In the island of Ireland, the Single Electricity Market (SEM) aims to deliver high levels of supply security, reliability, and transparency through multiple markets with different trading time frames and clearing procedures. This paper proposes a powerful methodology to maximize the revenues from the participation in the SEM. A forecasting model of four successive stages based on neural networks is proposed to predict the demand and system marginal prices of the SEM ex-ante markets. An energy arbitrage optimization framework is proposed for battery energy storage systems (BESS) to maximize the arbitrage profits. The methodology efficacy is validated by achieving 91.1% selling accuracy, 97.9% buying accuracy, and 85.1% energy arbitrage net accuracy of the ideal case where the SEM data is perfectly-known for three consecutive months. Furthermore, the BESS degradation is evaluated and a cost-benefit analysis is introduced to evaluate the economic feasibility of BESS participating in the SEM ex-ante markets. The results reveal that the participation of BESS in the SEM solely is not profitable, however, under stacked revenues arrangement, the proposed methodology can be applied to boost the BESS revenues.

Stochastic energy management of an electricity retailer with a novel plug-in electric vehicle-based demand response program and energy storage system: A linearized battery degradation cost model

This study investigates the stochastic optimal electricity procurement problem of an electricity retailer in a smart grid environment. Various energy provision means, including distributed generations (DGs), pool market (PM), forward contracts (FC), photovoltaic (PV) system, wind turbine (WT), and Battery energy storage system (BESS) are considered. A novel plug-in electric vehicle (PEV) based demand response program (DRP) is integrated into the problem considering economic incentives for PEV owners, which is computationally fast, and it is based on the uncertainties derived from PEVs, such as arrival/departure time, daily traveled miles and vehicle type. Moreover, the uncertainty of PM price, solar irradiation, wind speed, and conventional loads are taken into account by an artificial neural network (ANN)-based scenario generation method. Furthermore, an analytical linear BESS degradation cost model is integrated into the objective function to ensure fast global optimization. To confirm the functionality of the proposed method, different operation schemes, such as with and without DRP, with and without battery degradation cost, are thoroughly inspected. Additionally, the sensitivity of the problem for the number of PEVs is analyzed. The proposed two-stage stochastic mixed-integer linear problem (MILP) is solved by commercially available optimization software.

Robust Operation of Hybrid Solar–Wind Power Plant with Battery Energy Storage System

The intraday continuous electricity market (ICM) is a potential target market for the Dispatchable Hybrid Renewable solar–wind–battery energy storage system (BESS) power plant (DHRB). However, the uncertainty of the electricity price jeopardizes economic justification of BESS operation, an essential component of DHRB. Using the duality theory, this paper proposes a unilevel mixed-integer linear programming rolling-approach-based robust optimal scheduling tool for DHRB that keeps BESS operation optimal should the worst price scenario occur. It reflects BESS’s degradation as penalty factors and also integrates a BESS degradation model in the scheduling tool for better assessment of the available resources through the BESS’s lifetime. This tool aids the DHRB operator to decide the power offer to the ICM in such a way that the BESS’s operation remains optimal. A case study is carried out to demonstrate the application of the proposed tool. Both the long-term and short-term losses/benefits of utilizing this tool for scheduling DHRB in the ICM are investigated at various uncertainty levels. It is shown that there will be a risk of loss of income for the DHRB in the short-term due to increased nondispatchable energy. However, by limiting the use of BESS to only those settlement periods that are either certainly profitable or unavoidable, the lifetime of BESS can potentially be extended. Hence, this can result in more income by the DHRB power plant in the long-term.

A vision of flexible dispatchable hybrid solar‐wind‐energy storage power plant

Dispatchable renewable generation is essential for 100% renewables power system. It is in the interest of both power system operators and customers since it reduces the need for flexibility and reserve. To this end, battery energy storage systems (BESS) are proposed for integration in the renewable power plant. This paper presents the optimal dispatch unit for a dispatchable hybrid solar-wind power plant with BESS framework. It achieves optimal dispatchable renewable generation (from dispatchable hybrid renewable (solar-wind) power plant with BESS, DHRB, operator perspective), subject to operational limits, by exploiting the synergy of wind and solar energy and combining it with storage capability of BESS using two different operation strategies, maximisation of revenues and maximisation of renewables harvesting. A continuous BESS degradation model is incorporated in the proposed rolling-algorithm-based-optimal dispatch unit to improve the accuracy of results. The applicability of the proposed methodology and the performance of the operation strategies are demonstrated using a case study and the operation strategies are compared. Further, the effect of BESS size on its degradation and dispatchable power in a hybrid solar-wind power plant with BESS are investigated through BESS lifetime. An indicative economic analysis is carried out to provide ground for the extra investment on and sizing of BESS for a dispatchable hybrid renewable power plant.

Decentralized V2G/G2V Scheduling of EV Charging Stations by Considering the Conversion Efficiency of Bidirectional Chargers

With a rapid increase in the awareness of carbon reduction worldwide, the industry of electric vehicles (EVs) has started to flourish. However, the large number of EVs connected to a power grid with a large power demand and uncertainty may result in significant challenges for a power system. In this study, the optimal charging and discharging scheduling strategies of G2V/V2G and battery energy storage system (BESS) were proposed for EV charging stations. A distributed computation architecture was employed to streamline the complexity of an optimization problem. By considering EV charging/discharging conversion efficiencies for different load conditions, the proposed method was used to maximize the operational profits of each EV and BESS based on the related electricity tariff and demand response programs. Moreover, the behavior model of drivers and cost of BESS degradation caused by charging and discharging cycles were considered to improve the overall practical applicability. An EV charging station with 100 charging piles was simulated as an example to verify the feasibility of the proposed method. The developed algorithms can be used for EV charging stations, load aggregators, and service companies integrated with distributed energy resources in a smart grid.

Economic evaluation of operation strategies for battery systems in football stadiums: A Norwegian case study

This paper describes a techno-economic evaluation of case studies performed at the Skagerak EnergyLab. The EnergyLab consists of a 1.1 MWh battery energy storage system (BESS) and a 800 kWp photovoltaic (PV) power plant installed in a football stadium. The aim of this paper is to analyse the installation’s performance by studying a variety of cases involving operation strategies for peak shaving, self-consumption maximisation, energy arbitrage and feed-in limitation. The software tool SimSES is used to simulate BESS degradation. Moreover, the Norwegian economic and regulatory framework is used as a basis for an economic evaluation. An important outcome of this work is that a BESS that offers stacked value by combining peak shaving, energy arbitrage, self-consumption and the replacement of a backup diesel generator, may represent a feasible option in Norway. The techno-economic analysis also demonstrates that the profitability is heavily dependent on the operation strategy of the BESS.

(Invited) Battery Durability and Reliability Under Electric Utility Grid Operations

Battery Energy Storage Systems (BESSs) show promise to help renewable energy sources integration onto the grid. These systems are expected to last for a decade or more, but the actual battery degradation under different real-world conditions is still largely unknown.In this work we analyzed different types of usage for batteries similar to deployed 1MW and 2MW BESS in the Hawaiian archipelago. These batteries are used for a mix of different applications such as voltage smoothing, curtailment, reserve and frequency regulation. With different usages come different battery degradation. A new protocol encompassing all those applications was designed to be able to test the cells and assess their durability in the field. We investigated the impact of frequency regulation duty cycles with different SOC ranges, depth of discharge and temperatures. We also investigated cells with varying duty cycles and temperatures to decipher whether the degradation can be predicted from a laboratory experiment.Based on the BESS representative usage profiles, we designed cycle-aging and calendar-aging experiments to test the degradation in a controlled fashion. The frequency regulation duty cycle was extracted from a 3 years usage analysis of a previous generation lithium titanate BESS in operation in Hawai'i since 2012. Laboratory testing was used to investigate the degradation in detail to improve remaining useful life predictions. The degradation mechanisms, and their path dependency, were analyzed using incremental capacity analysis and mechanistic modeling. It was found that despite the fact that most of the cells did less than 0.5% capacity after more than 400 days of testing, they actually degraded significantly. Cell temperature had the strongest impact on the loss of lithium inventory whereas duty cycle had a big impact on the loss of positive electrode material. Interestingly, the impact of SOC in the calendar aging experiments, was found to be counterintuitive as batteries lost capacity faster at low SOCs than at high. Based on our results we established a 20 year forecast of the BESS degradation under different usages.

Interval Optimization Based Coordination of Demand Response and Battery Energy Storage System Considering SOC Management in a Microgrid

Microgrids can effectively integrate distributed generation (DG) to supply power to local loads. However, uncertainties from renewable DG and loads may lead to increased operating costs or operating constraint violations. To solve these issues, this paper proposes a two-stage coordination approach of price-based demand response (PBDR) and battery energy storage systems (BESSs) to minimize the total operating cost and enhance operational reliability. In the first stage, day-ahead PBDR is scheduled, aiming to shift loads to improve renewable energy utilization efficiency. Considering limited prediction horizon of uncertainties when dispatching BESSs, hourly state of charge (SoC) limits are also optimized over the whole-day horizon with consideration of BESS degradation cost in the day-ahead stage. Then in the second stage, the BESSs are dispatched hourly within the optimized SoC limits to track uncertainty realization and compensate the first-stage PBDR decisions. Furthermore, a two-stage interval optimization (TSIO) method is proposed to formulate the problem. Accordingly, a solution algorithm is developed to coordinately solve the two operation stages under the uncertainties. The proposed coordination approach is verified with uncertainty realization scenarios. The results indicate the high energy utilization efficiency and strong operational reliability of the proposed coordination approach.