Peak Demand Research Articles

From PV to EV: Mapping the Potential for Electric Vehicle Charging with Solar Energy in Europe

The electrification of the transport sector is a key element in decarbonizing our societies. However, energy systems will have to cope with additional electricity demand due to the charging needs of electric vehicles (EVs) together with the integration of fluctuating renewable energy sources. Shifting EV charging during the day can contribute to absorbing photovoltaic (PV) production peaks and limiting the additional demand during peak periods. The EV batteries can then be discharged to the grid or home during the evening or other demand peaks. We developed a new methodology to quantify the flexibility gained from the EV-PV coupling from local mobility habits and local (or decentralized) photovoltaic production. Our approach first focuses on the geospatial modelling of mobility habits across Europe to quantify energy demand for charging. The charging demand is then distributed between residential areas, workplaces and points of interest (shopping, leisure, etc.) to model the spatiotemporal distribution of energy needs. We show and discuss a practical case in Copenhagen illustrating the impact of charging behaviour for three different scenarios. The methodology is implemented as a calculation module in the open-source online geographic tool for energy transition planning, Citiwatts.

Optimal Placement and Capacity of BESS and PV in EV Integrated Distribution Systems: The Tenth Feeder of Phitsanulok Substation Case Study

Installing a battery energy storage system (BESS) and renewable energy sources can significantly improve distribution network performance in several aspects, especially in electric vehicle (EV)-integrated systems because of high load demands. With the high costs of the BESS and PV, optimal placement and capacity of them must be carefully considered. This work proposes a solution for determining the optimal placement and capacity of a BESS and photovoltaic (PV) in a distribution system by considering EV penetrations. The objective function is to reduce system costs, comprising installation, replacement, and operation and maintenance costs of the BESS and PV. The replacement cost is considered over 20 years, and the maintenance and operation costs incurred in the distribution system include transmission line loss, voltage regulation, and peak demand costs. To solve the problem, two metaheuristic algorithms consisting of particle swarm optimization (PSO) and the African vulture optimization algorithm (AVOA) are utilized. The tenth feeder of Phitsanulok substation 1 (PLA10), Thailand, which is a 91-bus distribution network, is tested to evaluate the performance of the proposed approach. The results obtained from the considered algorithms are compared based on distribution system performance enhancement, payback period, and statistical analysis. It is found from the simulation results that the installation of the BESS and PV could significantly minimize system cost, improve the voltage profile, reduce transmission line loss, and decrease peak demand. The voltage deviation could be reduced by 86%, line loss was reduced by 0.78 MW, and peak demand could be decreased by 5.706 MW compared to the case without BESS and PV installations.

The Role of Domestic Heat Pumps in Providing Flexibility to the UK Electricity System

Widespread adoption of residential heat pumps is predicted to create challenges for national and local electricity systems. Flexible operation of heat pumps could help smooth peak demand and better utilise renewables. Achieving these benefits involves many stakeholders from the heat pump and electricity sectors with different perspectives and expectations. This work brought together 52 experts from different parts of the UK system to discuss and debate the role of heat pump flexibility in a decarbonised electricity system in 2035. A co-production research model was adopted, designed to integrate diverse forms of knowledge and perspectives in the co-production of knowledge on heat pump flexibility. A series of participatory activities were undertaken including a one-day workshop. Elements of a common vision emerged, such as the anticipated widespread flexible operation of heat pumps as the cheapest way of running a heat pump and the likelihood of a highly automated and remote-controlled manner of operation. Disagreements and unknowns also emerged. This work aims to support stakeholders in planning for the social, technical and economic aspects of flexible heat pump operation in their own organisations.

Frequency and intensity of maximal and submaximal demanding scenarios in U19 professional soccer players

ABSTRACT This study examined the frequency of the most demanding scenarios (MDS) during official soccer matches using rolling averages over 1, 5, and 10 min. Forty-two Under-19 players from different positions (central defenders, full-backs, central midfielders, wide midfielders, offensive midfielders, and forwards) were monitored across 27 matches using GPS to track distance covered, high-speed running, sprint distance, accelerations, and decelerations. Intensity thresholds were established based on percentiles (0–25, 25–50, 50–75, 75–100, and ≥100). The main findings suggest that: (i) Peak Demands occur in less than 1% of all time windows and variables for all positions; (ii) Most efforts occur below peak demands, with around 95% for high-speed running and sprint distance, and 85% for accelerations, decelerations, and total distance; (iii) Significant differences in intensity distributions were found between positions, particularly at medium-low and high intensities. Regarding training prescription, relying solely on MDS may be limited, highlighting the need to supplement MDS with other metrics for a comprehensive understanding of match demands. This approach ensures better-informed training programs for soccer players.

Smart Operation Control of Power and Heat Demands in Active Distribution Grids Leveraging Energy Flexibility

Demand flexibility plays a crucial role in mitigating the intermittency of renewable power sources. This paper focuses on an active distribution grid that incorporates flexible heat and electric demands, specifically heat pumps (HPs) and electric vehicles (EVs). Additionally, it addresses photovoltaic (PV) power generation facilities and electrical batteries to enhance demand flexibility. To exploit demand flexibility from both heat and electric demand, along with the integration of PVs and batteries, Control and Communication Mechanisms (CCMs) are formulated. These CCMs integrate demand flexibility into the distribution grids to obtain economic benefits for private households and, at the same time, facilitate voltage control. Concerning EVs, the paper discusses voltage-based droop control, scheduled charging, priority charging, and up-/down-power regulation to optimize the charging and discharging operations. For heat demands, the on-off operation of the HPs integrated with phase change material (PCM) storage is optimized to unlock heat-to-power flexibility. The HP controllers aim to ensure as much self-consumption as possible and provide voltage support for the distribution grid while ensuring the thermal comfort of residents. Finally, the developed CCMs are implemented on a small and representative community of an active distribution grid with eight houses using Power Factory software and DIgSILENT simulation language (DSL). This scalable size of the active distribution network facilitates the careful study of symbiotic interaction among the flexible load, generation, and different houses thoroughly. The simulation results confirm that the integration of flexible demands into the grid using the designed CCMs results in the grid benefiting from stabilized voltage control, especially during peak demand hours.

PEMFC Electrochemical Degradation Analysis of a Fuel Cell Range-Extender (FCREx) Heavy Goods Vehicle after a Break-In Period

With the increasing focus on decarbonisation of the transport sector, it is imperative to consider routes to electrify vehicles beyond those achievable using lithium-ion battery technology. These include heavy goods vehicles and aerospace applications that require propulsion systems that can provide gravimetric energy densities, which are more likely to be delivered by fuel cell systems. While the discussion of light-duty vehicles is abundant in the literature, heavy goods vehicles are under-represented. This paper presents an overview of the electrochemical degradation of a proton exchange membrane fuel cell integrated into a simulated Class 8 heavy goods range-extender fuel cell hybrid electric vehicle operating in urban driving conditions. Electrochemical degradation data such as polarisation curves, cyclic voltammetry values, linear sweep voltammetry values, and electrochemical impedance spectroscopy values were collected and analysed to understand the expected degradation modes in this application. In this application, the proton exchange membrane fuel cell stack power was designed to remain constant to fulfil the mission requirements, with dynamic and peak power demands managed by lithium-ion batteries, which were incorporated into the hybridised powertrain. A single fuel cell or battery cell can either be operated at maximum or nominal power demand, allowing four operational scenarios: maximum fuel cell maximum battery, maximum fuel cell nominal battery, nominal fuel cell maximum battery, and nominal fuel cell nominal battery. Operating scenarios with maximum fuel cell operating power experienced more severe degradation after endurance testing than nominal operating power. A comparison of electrochemical degradation between these operating scenarios was analysed and discussed. By exploring the degradation effects in proton exchange membrane fuel cells, this paper offers insights that will be useful in improving the long-term performance and durability of proton exchange membrane fuel cells in heavy-duty vehicle applications and the design of hybridised powertrains.

Current State and Future Potential of Energy Efficiency and Demand Side Management in the South African Context

This paper explores the current state and future potential of Energy Efficiency and Demand Side Management (EEDSM) in South Africa. With a heavy reliance on coal for electricity generation, South Africa faces significant environmental and sustainability challenges. Integrating renewable energy sources like wind, water, and solar (WWS) is crucial for reducing greenhouse gas emissions and ensuring energy security. The study highlights the importance of demand-side management in optimising energy consumption and mitigating peak demand pressures. The South African government has implemented various policies and regulatory frameworks to promote EEDSM, focusing on reducing energy consumption through technological innovations and strategic planning. Models such as the Five-Stage Model for Computer Technology Integration, the E-Capacity Model, and the Systemic Planning Model for ICT Integration provide structured approaches to integrating advanced technologies and practices into energy systems. The research includes a comprehensive analysis of South Africa's current energy mix, predominantly coal and nuclear, and projects a shift towards a diversified portfolio dominated by renewable sources by 2050. This transition is expected to create substantial employment opportunities and enhance energy efficiency. Case studies and success stories from both the public and private sectors underscore the economic and environmental benefits of EEDSM initiatives. South Africa can significantly improve its energy efficiency and sustainability by leveraging innovative energy technologies, such as smart grids and smart metering. The findings emphasise the need for continued investment in renewable energy and demand- side management to achieve a more resilient and efficient energy sector.

Optimal design of a hinge-spring-friction device for enhancing wind induced structural response of onshore wind turbines

Wind turbines are subjected to fluctuating wind loads leading to considerable forces and structural fatigue to the towers, ultimately affecting costs, performances, and lifespan, for the tower as well as for the foundation. To mitigate these issues, both in terms of peak and fatigue structural demand, this paper presents the development and optimization of a Hinge-Spring-Friction Device (HSFD) designed for onshore wind turbines. A decoupled numerical model of the wind turbine system incorporating the HSFD is first established. The wind load is modelled by means of the open-source software QBlade© accounting for different wind conditions. These loads are then applied to a FEM structural model of the wind turbine developed in Simulink© and optimized for computational efficiency. The optimal design parameters (strength and stiffness of the frictional and elastic part, respectively) of the HSFD are determined through a multi-objective constrained optimization algorithm, minimizing the peak base moment and total damage fatigue. The proposed framework is then applied to a NREL 5 MW wind turbine to provide an applicative example. The results show that the optimized HSFD can significantly reduce the fatigue damage and the base moment demand to the tower, so providing a really promising solution for the effective design of wind turbines as well as for the repowering of existing plants.

Development and performance evaluation of active insulation systems using solid-state thermal switches

Traditional building envelopes have passive insulation systems that cannot respond to dynamic changes in the environment. An Active Insulation System (AIS) consists of Active Insulation Materials (AIMs) that dynamically vary the thermal conductivity of the insulation system. Several researchers have evaluated the impact of AIS on building thermal and energy performance by using simulation tools. Up to 70% savings in annual heating and cooling energy and significant reductions in peak demand have been predicted for some climates with wall systems employing AIS. However, materials and assembly development still need a cost-effective product that achieves the required performance. In this study, we present the process of developing an AIS that we will install in a test hut for its performance evaluation. Minimum performance criteria of the AIS system are developed based on R-low/R-high ratio, required time and efficiency to switch states, and cost estimates. The following steps during this study are creating the concept to meet the requirements, predicting the performance via simulations, developing the experimental setup for bench-scale testing, and finally, constructing a full-scale wall assembly and monitoring the performance when exposed to environmental chamber tests. The selected approach uses off-the-shelf products to create an AIS that can switch R-value between 0.98 ft2·°F·h/BTU (0.173 m2·K/W) and 5.81 ft2·°F·h/BTU (1.02 m2·K/W) and have a switching time of less than one minute between R-high and R-low.

Vehicle-to-Vehicle Energy Trading Framework: A Systematic Literature Review

As transportation evolves with greater adoption of electric vehicles (EVs), vehicle-to-vehicle (V2V) energy trading stands out as an important innovation for managing energy resources more effectively as it reduces dependency on traditional energy infrastructures and, hence, alleviates the pressure on the power grid during peak demand times. Thus, this paper conducts a systematic review of the V2V energy trading frameworks. Through the included article analysis (n = 61), this paper discusses the state-of-the-art energy trading frameworks’ structure, employed methodologies, encountered challenges, and potential directions for future research. To the best of the authors’ knowledge, this is the first review explicitly focused on V2V energy trading. We detail four critical challenges to face while establishing the framework in current research, providing an overview of various methodologies, including auctions, blockchain, game theory, optimisation, and demand forecasting, that are used to address these challenges and explore their integration within the research landscape. Additionally, this paper forecasts the evolution of V2V energy trading, highlighting the potential incorporation of advanced and established technologies like artificial intelligence (AI), digital twins, and smart contracts. This review aims to encapsulate the existing state of V2V energy trading research and stimulate future advancements and technological integration within the field.

Optimal demand response scheduling and voltage reinforcement in distribution grids incorporating uncertainties of energy resources, placement of energy storages, and aggregated flexible loads

Instead of expanding power plant capacities, which is an extremely expensive investment option, demand response offers an economical solution to the challenges arising from the variability and intermittency of the renewable energy resources and demand variations, particularly during demand peak periods. This paper proposes a multi-objective optimization framework for the optimal power flow problem that integrates a stepwise demand response involving flexible and aggregated loads. The process includes short-term demand forecasting using long short-term memory (LSTM) networks in a smart distribution grid, followed by the optimal allocation of energy storage systems, and load aggregators. By determining the optimal solution point of the multi-objective problem analytically, significant system costs and peak demand can be reduced without compromising system stability. Through numerical studies for a sample study case, a reduction of 22% in system costs, 2% in total voltage variation, and 10% in peak demand is observed for a negligible impact on customers’ convenience.

The strategy of differential pricing and network utilization: evidence from India’s telecommunications sector

PurposeThe network capacity deployed to manage the busy hour (or peak-hour) traffic remains underused during the nonbusy (off-peak) hours. Transferring some traffic from peak-hour to off-peak hours is likely to improve the utilization of network resources during the off-peak hours. This paper aims to examine whether diverting traffic from peak-hour to off-peak hours is possible by adopting the differential pricing policy.Design/methodology/approachThe peak-load pricing theory suggests that the policy of differential pricing is socially optimal when there is peak demand for a particular duration and then there is off-peak demand. In this study, hourly traffic data from both peak and off-peak periods were collected from the Indian telecom service provider, “Aircel.” The paper analyzed the disparity in traffic between peak and off-peak hours using the nonparametric Tukey’s test. An experiment was also conducted to analyze whether a significant shift in telecom traffic occurs from the peak to the off-peak period when a price discount is applied during the off-peak period.FindingsStatistically significant differences were observed in network traffic between peak-hour and off-peak hours. The network utilization of the telecom service provider Aircel was notably lower, particularly during the off-peak hours. The experiment demonstrated a high degree of price sensitivity among telecom service subscribers. Telecom Regulatory Authority of India (TRAI) has not considered network utilization of telecom service providers as a key performance indicator. Based on the outcomes of the study, this paper recommends that TRAI should adopt a more proactive approach by encouraging telecom service providers to follow the policy of differential pricing to enhance utilization of their network capacity.Originality/valueTo the best of the authors’ knowledge, this is the first paper to explore the issue of pricing as a tool for bringing about more uniform movement of telecom traffic, thereby enhancing network utilization within India’s telecommunications sector.

Technical and Economic Energy Optimization of the Energy Hub System with Considering Peak Demand Pricing in the Local Energy Markets

Technical and Economic Energy Optimization of the Energy Hub System with Considering Peak Demand Pricing in the Local Energy Markets

A cloud-oriented data-analysis framework to analyze peak demand dynamics in institutional building clusters

Peak loads in higher education institutional building clusters (IBCs) possess considerable economic repercussions on their overall operations. Thus, identifying electrically inefficient buildings presents a significant opportunity to curtail peak loads and promote energy efficiency in IBCs. Existing literature implements clustering algorithms to comprehend the electrical demand dynamics of buildings to analyze disparity in their load behavior. These techniques perform well in single building environment, however, fall short in comprehending the demand dynamics for building clusters, specifically during peak loads. This study introduces a cloud-oriented quantile-based data analysis framework, specifically designed for simultaneously evaluating demand profiles of multiple buildings within IBCs. Quantile-based metrics namely, Value-at-Risk, Conditional Value-at-Risk and Conditional Value-at-Risk standard deviation are implemented to comprehend the electrical fluctuations and quantify the electrical impact of each building during campus-wide peak demands. A cross-building comparison is established by linearly ranking buildings following two key criteria: (i) buildings with frequent demand fluctuations and (ii) buildings exerting a high electrical impact on overall IBC demand. Both criteria are equally weighted while ranking to identify the most inefficient buildings during peak loads. The framework is implemented in a Canadian university and resulted a substantial 50 MW demand reduction through recommissioning and retrofitting of inefficient buildings.

Enhancing sustainable and climate-resilient agriculture: Optimization of greenhouse energy consumption through microgrid systems utilizing advanced meta-heuristic algorithms

Greenhouses offer controlled microclimates that enable year-round cultivation, improving food security and agricultural productivity. However, greenhouses are energy-intensive, with heating accounting for a significant portion of the associated costs. This study explores optimal microgrid configurations, economic viability, and policy recommendations for sustainable greenhouse agriculture in Nigeria. An in-depth energy assessment of a reference greenhouse in a South Korean facility is conducted. Distinct climatic differences between South Korea and Nigeria are highlighted, emphasizing the need for tailored greenhouse designs and energy solutions. Shifting focus to Nigeria, this study investigates the feasibility of hybrid renewable energy systems with a focus on wind and solar power across six geopolitical zones in Nigeria. The analysis encompasses technical, economic, and policy aspects, providing a holistic perspective on renewable energy adoption. Notably, the study uses an advanced optimization model, Teaching and Learning–Based Optimization algorithm, to assess the net present cost and baseload supply reliability, offering valuable insights for investors and policymakers. The result indicates diverse energy requirements across Nigeria, with total monthly peak energy demands ranging from 5374.80 kWh in the Southeast to 17,115.76 kWh in the Northwest, and a notable variation in the Levelized Cost of Electricity (LCOE), with the lowest at $0.07327 in Kano. Specifically, in Ogun, the net present cost for the WT-PV-ESS system stood at $520,935.45, while the PV-ESS system cost was substantially lower at $500,444.41. This confirms the effectiveness of location-specific analysis and shows the suitability of photovoltaic–battery energy storage systems for Nigeria's diverse regions, with unique considerations for specific areas. Policy recommendations, including feed-in tariffs, renewable portfolio standards, net metering, research support, and market development, provide a holistic framework for the adoption of renewable energy and sustainable agriculture. Improving infrastructure, market access, and financing for smallholder farmers is integral for improving food security and standards of living in rural Nigeria. In conclusion, Nigeria can leverage renewable resources to revolutionize its energy and agriculture sectors, setting an example for a sustainable and resilient future.

Design of a model predictive control methodology for integration of retrofitted air‐based PV/T system in school buildings

This paper presents a model predictive control (MPC) methodology for integrating air-based photovoltaic/thermal (PV/T) systems in school buildings. The methodology is developed based on a case study for an archetype fully electric school building in Québec, Canada. A data‐driven grey‐box model for the classrooms is calibrated with measured data, and a PV/T model is developed. These models are integrated to apply MPC to the school building using the established dynamic tariffs for morning and evening peaks. Three scenarios are investigated and compared: (1) A reference case without a PV or PV/T system, (2) Integration of a PV system and MPC, and (3) Integration of a PV/T system and MPC under a demand response scenario. Results show that using the MPC with PV/T integration can reduce peak demand by up to 100% during high-demand periods for the grid. This methodology is scalable and can be transferable to other institutional buildings. Abbreviations: PV: Photovoltaics system; BIPV/T: Building integrated photovoltaic and thermal system; COP: Coefficient of performance; CV-RMSE: Root-mean-square error; DR: Demand response; DSM: Demand-side management; HRV: Heat recovery ventilator; HVAC: Heating ventilation and air conditioning; IEQ: Indoor environmental quality; MPC: Model predictive control; RC model: Resistance-capacitance model; RES: Renewable energy sources

Optimal load scheduling strategy for isolated building-integrated DC microgrid using binary PSO to maximize user comfort and minimize peak-to-average ratio

Due to the growth of electricity consumption in residential and commercial buildings, there is a growing burden on utilities in urban areas. Sustainable buildings are being developed by integrating renewable energy generation to make an eco-friendly environment. An isolated building-integrated (IBI) DC microgrid fed by a hybrid solar PV (photo voltaic) generator-thermo electric generator (TEG) is one viable option for obtaining self-sustainable energy for the building. The battery is an important component in such IBI DC microgrid fed by the hybrid solar PV generator-TEG for providing reliable power to the loads. However, the operation and planning of the IBI DC microgrid are challenging, and therefore it needs a suitable load scheduling strategy. This paper proposes the optimal load scheduling strategy (OLSS) under demand response to schedule the household loads in the building. The proposed OLSS aims to balance two conflictive objectives, user comfort and peak-to-average ratio (PAR), using a binary particle swarm optimization algorithm (BPSO). The proposed OLSS optimizes user comfort and PAR and reduces peak demand and battery usage. It also helps in the optimum utilization of power generated from hybrid solar PV generator-TEG by shifting the loads during sun hours. Reducing battery usage reduces the conversion loss and extends the battery life. To validate the proposed OLSS, the investigations are carried out using the data obtained across different seasons. The results show that the proposed OLSS schedules the household loads with the desired user comfort and reduces PAR by 22 % in future buildings.

Deep neural network optimization of a continuous solar-geothermal-driven plant with integrated thermal and mechanical energy storage: Incorporating bypass mechanism

In this current investigation, optimizing the cost and technological aspects of a novel integrated dual energy storage system embedded in a solar-geothermal-driven plant is proposed to assist in generating inexpensive and continuous power, fresh water, and hydrogen. the devised compressed air energy storage unit, aims to store a portion of the compressed air in the storage tank to use later for low-priced electricity production in peak demand times, and via a Bypass, the rest of the compressed air is used to run the downward sub-cycles even during the charging phase of the compressed air energy storage tank. The novel introduction of a Bypass in the compressed air energy storage unit, accelerates the synergy of the system by enabling the compressed air energy storage unit to continuously provide pressurized air to the system, instead of only supplying pressurized air for a limited time. Therefore, this will largely contribute to an increase in the production of the system. The introduction of the Bypass in the CAES unit has resulted in a significant 20.36 % increase in its round-trip efficiency, with only a negligible 2.8482 % rise in the overall cost rate, and a major 44.05 % increase in the rate of hydrogen production. The heated compressed air by the heliostat field is stored in the phase change material tank to supply the gas turbine with a constant heat load. Additionally, geothermal energy as an auxiliary energy source helps to generate desired products. A parametric study investigates how design parameters affect system performance. Through deep learning optimization which involves artificial neural networks and genetic algorithms, reduces the time used in optimization as it helps in finding the best solution fast and efficient. Based on multi-objective optimization, the system achieves a round-trip efficiency of 46 %, produces 6.956 kg/day of hydrogen, and has a total cost rate of 1.46 $/s.

Water consumption in absorption chillers is not negligible: Water-for-cooling consumption of chiller systems for commercial buildings in the United States

We compare peak electricity demand and water-for-cooling consumption of two electric chillers –air-cooled and water-cooled– to that of a natural gas-fired heat-driven chiller – an absorption chiller – in the United States. We develop a mass-and-energy-balance model in which each chiller supplies the cooling demands of 16 commercial building types in 15 climate zones of the contiguous US. We quantify the water-for-cooling of each chiller within two categories: (1) ‘cooling and power’ (C&P) – the sum of water consumed directly by each chiller and water consumed at the point of power generation; and (2) ‘total’ – the sum of C&P water consumption and water consumption upstream from the power generation. The air-cooled, water-cooled and absorption chillers consume an average of 2.43, 3.73 ± 0.25, and 3.78 ± 0.35 m3 of C&P water per MWh of cooling, respectively. They consume an average of 9.26, 8.32 ± 0.25, and 3.89 ± 0.34 m3 of total water per MWh of cooling, respectively. That is, life cycle water consumption for natural gas-based absorption chilling is not negligible, though it is lower than for the electricity-based chillers under current grid conditions. Lower power grid life cycle water consumption, e.g., under decarbonization, could change this relationship.

Simulation test of 50 MW grid-connected “Photovoltaic+Energy storage” system based on pvsyst software

With the implementation of the national “double carbon” strategy, the installed capacity of new energy power generation continues to grow, and stable photovoltaic power generation solutions have received increasing attention. The PV + energy storage system with a capacity of 50 MW represents a certain typicality in terms of scale, which is neither too small to show the characteristics of the system nor too large to simulate and manage. This study builds a 50 MW “PV + energy storage” power generation system based on PVsyst software. A detailed design scheme of the system architecture and energy storage capacity is proposed, which is applied to the design and optimization of the electrochemical energy storage system of photovoltaic power station. Based on the results of PVsyst operation simulation test, the operation performance of 50 MW “PV + energy storage” power generation system is explored. The results show that the 50 MW “PV + energy storage” system can achieve 24-h stable operation even when the sunshine changes significantly or the demand peaks, maintain the balance of power supply of the grid, and save a total of 1121310.388 tons of CO2 emissions during the life cycle of the system. The various parts of the system, including the photovoltaic array, the energy storage unit and the grid interface, demonstrated efficient collaborative performance in the simulation environment of PVsyst.The analysis of power generation shows obvious seasonal changes. The unit power generation is higher from March to September, and the more the power generation is the overall annual power generation shows good consistency and predictability under the regulation of the energy storage system. Through the analysis of different operating scenarios, the key parameters that affect the system performance are further determined, such as lighting conditions, battery storage capacity, power consumption device efficiency. For lighting conditions, the total radiation amount of the incident lighting surface takes the most proportion in the interval of 380–900 W/m2 and 25-60kwh/m2/Bin. The simulation test also reveals the important role of energy storage unit in power grid demand peaking and valley filling, which has an important impact on balancing the instability of photovoltaic power generation and improving the system response ability.