Optimal Operation Strategy Research Articles

Genetic programming expressions for effluent quality prediction: Towards AI-driven monitoring and management of wastewater treatment plants

Continuous effluent quality prediction in wastewater treatment processes is crucial to proactively reduce the risks to the environment and human health. However, wastewater treatment is an extremely complex process controlled by several uncertain, interdependent, and sometimes poorly characterized physico-chemical-biological process parameters. In addition, there are substantial spatiotemporal variations, uncertainties, and high non-linear interactions among the water quality parameters and process variables involved in the treatment process. Such complexities hinder efficient monitoring, operation, and management of wastewater treatment plants under normal and abnormal conditions. Typical mathematical and statistical tools most often fail to capture such complex interrelationships, and therefore data-driven techniques offer an attractive solution to effectively quantify the performance of wastewater treatment plants. Although several previous studies focused on applying regression-based data-driven models (e.g., artificial neural network) to predict some wastewater treatment effluent parameters, most of these studies employed a limited number of input variables to predict only one or two parameters characterizing the effluent quality (e.g., chemical oxygen demand (COD) and/or suspended solids (SS)). Harnessing the power of Artificial Intelligence (AI), the current study proposes multi-gene genetic programming (MGGP)-based models, using a dataset obtained from an operational wastewater treatment plant, deploying membrane aerated biofilm reactor, to predict the filtrated COD, ammonia (NH4), and SS concentrations along with the carbon-to-nitrogen ratio (C/N) within the effluent. Input features included a set of process variables characterizing the influent quality (e.g., filtered COD, NH4, and SS concentrations), water physics and chemistry parameters (e.g., temperature and pH), and operation conditions (e.g., applied air pressure). The developed MGGP-based models accurately reproduced the observations of the four output variables with correlation coefficient values that ranged between 0.98 and 0.99 during training and between 0.96 and 0.99 during testing, reflecting the power of the developed models in predicting the quality of the effluent from the treatment system. Interpretability analyses were subsequently deployed to confirm the intuitive understanding of input-output interrelations and to identify the governing parameters of the treatment process. The developed MGGP-based models can facilitate the AI-driven monitoring and management of wastewater treatment plants through devising optimal rapid operation and control schemes and assisting the plants’ operators in maintaining proper performance of the plants under various normal and disruptive operational conditions.

Technical analysis of a novel economically mixed CO2-Water enhanced geothermal system

Geothermal energy has undoubtedly been considered a promising low-carbon resource in future energy supply responding to the rising energy demand and severe climate concerns. CO2 has been demonstrated as a comparable alternative for water in developing an Enhanced geothermal system (EGS), which simultaneously unlocks the potential to sequester CO2 against climate concerns. However, an earlier thermal breakthrough is always observed owing to higher mobility in geothermal reservoirs for CO2-EGS, and an expensive cost of CO2 consumption makes CO2-EGS challenging to achieve economic profitability. In this study, a novel CO2-water-EGS is proposed to investigate the feasibility of solving the obstacles in conventional EGSs. An integrated analysis for multiple EGS scenarios is carried out from both the technical and economic perspectives. The effects of a CO2-water ratio, an injection rate and a flow scheme are comprehended. Followed by the determination of an optimal operating strategy. In the results, by reasonably selecting a CO2 mass ratio and an injection rate, CO2-water-EGS can recover more geothermal energy and earn more profits than water-EGS and CO2-EGS. The injection rate shows positive correlations to heat mining, carbon storage, and economic performance, while the impact of a flow scheme is negligible when the total volume of injected fluids remains consistent. From the optimization study, the CO2-water-EGS with the 110 kg/s injection rate and 20%wt CO2 ratio shows the highest Net Present Value (NPV) of 42.4 M$. This study proposes theoretical guidance to operators in their decision-making processes on the development of CO2-water-EGS.

Optimized Design of a H2-Powered Moped for Urban Mobility

Micro-mobility plays an increasingly important role in the current energy transition thanks to its low energy consumption and reduced contribution to urban congestion. In this scenario, fuel cell hybrid electric vehicles have several advantages over state-of-the-art battery electric vehicles, such as increased driving ranges and reduced recharge times. In this paper, we study the conversion of a commercial electric moped (Askoll eS3®) into a fuel cell hybrid electric vehicle by finding the optimal design of the components through an optimization methodology based on backward dynamic programming. This optimal design and operation strategy can also be implemented with a rules-based approach. The results show that a system composed of a 1 kW proton exchange membrane fuel cell, a 2000 Sl metal hydride hydrogen tank, and a 240 Wh buffer battery can cover the same driving range as the batteries in an electric moped (119 km). Such a hybrid system occupies considerably less volume (almost 40 L) and has a negligibly higher mass. The free volume can be used to extend the driving range up to almost three times the nominal value. Moreover, by using a high-pressure composite tank, it is possible to increase the mass energy density of the onboard energy storage (although compression can require up to 10% of the hydrogen’s chemical energy). The fuel cell hybrid electric vehicle can be recharged with green hydrogen that is locally produced. In detail, we analyze a residential scenario and a shared mobility scenario in the small Italian city of Viterbo.

Current Challenges in Operation, Performance, and Maintenance of Photovoltaic Panels

The installed solar capacity in the European Union has expanded rapidly in recent years. The production of these plants is stochastic and highly dependent on the weather. However, many factors should be considered together to estimate the expected output according to the weather forecast so that these new PV plants can operate at maximum capacity. Plants must be operated in coordination with maintenance operations and considering actual energy market prices. Various methods have recently been developed in the literature, ranging from the most impactful artificial-intelligence-based generation estimation methods to various diagnostic and maintenance methods. Moreover, the optimal operational and maintenance strategy usually depends on market regulation, and there are many concerns related to the distribution system operator. This review article aims to summarize and illustrate the challenges of operating and maintaining solar power plants and the economic and technical importance of these problems.

Feasibility of integrated solar photovoltaic pico‐pumped storage self‐sustained system for multi‐storey building in India

AbstractThis study explores the potential of utilizing a pico‐pumped storage system (PPSH) as an energy storage solution to enhance the integration of renewable energy sources in a multi‐story building. The study seeks to evaluate the system's ability to fulfil the energy requirements of a part of a multi‐storey building, considering factors such as cost‐effectiveness, longevity, and environmental impact. Basic relationships are used to model both floating solar PV systems and pumped storage systems in MATLAB. An algorithm based on linear programming is used to generate the optimal schedule, while a particle swarm optimization‐based algorithm is employed for the optimal design of the proposed system. In order to analyze the economic feasibility, the levelized cost of solar energy and the levelized cost of stored energy are estimated. An optimally sized floating solar PV integrated pico pumped storage system with an optimal operating strategy can perform technically. However, due to practical limitations, the system's dimensions cannot accommodate real‐world storage requirements. When levelized costs are considered, a pico‐pumped storage system becomes less appealing than battery storage systems, particularly if a new storage tank is to be constructed. Compared to battery storage systems, pumped storage systems have a lower energy density, which requires more space and height, especially in buildings. Pumped storage systems, on the other hand, have a longer lifespan and a lower environmental impact. Overall, the proposed system, along with an optimal scheduling algorithm, provides a long‐term solution for energy storage, aligning with the growing global emphasis on lowering carbon emissions and embracing renewable energy sources. Although there may be a high initial investment requirement and a restricted storage capacity, the sustained advantages of energy independence and a diminished carbon footprint render it an appealing option for environmentally conscious communities and green buildings.

Robust Operation of Flexible Distribution Network With Large-Scale EV Charging Loads

The increasing integration of electric vehicles (EVs) significantly challenges the efficient operation of distribution networks. With the rapid development of power electronic devices represented by soft open point (SOP), flexible distribution networks (FDNs) provide a promising opportunity for integrating large-scale EVs. In this paper, a robust operation method is proposed to improve the economy of EVs and ensure the flexible operation of FDN. First, an EVs-aggregator (EVs-AGG) model is formulated to incorporate diverse charging behaviors. The EVs-AGG is efficiently integrated into FDN through the DC link of multi-terminal SOP. Then, optimal operation strategies are obtained by coordinating EVs charging/discharging power with the regulation of multi-terminal SOP. Furthermore, considering the uncertainties of EVs and photovoltaics, a robust operation framework is established to deal with uncertainties. Finally, case studies based on a practical distribution network indicate that through the interactive power regulation between multi-terminal SOP and EVs-AGG, the proposed method alleviates the negative impacts caused by large-scale EVs integration and improves the operational flexibility of FDN.

Stochastic Flexible Resource Operations in Coordinated Green-Seaport Energy-Logistics Systems Using Constraint Generation Approach

The increasing penetration of renewable energy sources (RESs) in modern green seaports calls for more flexible management approaches that can minimize the daily seaport operation costs and RES curtailments. This paper establishes an optimal strategy in two-time intervals for flexible operations of energy storage systems and combined electric-thermal power demands of reefer containers in the coordinated green-seaport energy-logistics systems (ELS). For the day-ahead operation, a multi-stage stochastic optimization model is established to minimize the expected ELS operation cost according to the 24-hour-ahead forecasts. Uncertainties of RES power outputs, load demands, electricity prices, and ambient temperatures are considered in the optimization of day-ahead operation. For the intra-day operation, hydrogen, battery, and thermal storage systems as well as reefer containers operations are optimized based on the short-lead-time forecasts. Numerical simulations are carried out based on the Jurong seaport operation in Singapore to verify the effectiveness of the proposed optimal operation strategy for the coordinated green-seaport ELS. Simulation results reveal that the proposed strategy can achieve the lowest operation cost and the lowest RES curtailment in a green-seaport.

G-ACP: a machine learning approach to the prediction of therapeutic peptides for gastric cancer

Conventional Gastrointestinal (GI) cancer treatments are quite expensive and have major hazards. Nowadays, a different strategy places more emphasis on creating tiny biologically active peptides that do not cause severe poisoning. Anticancer peptides (ACPs) are found through experimental screening, which is time-dependent and frequently fraught with difficulties. Gastric ACPs are emerging as a promising GI cancer treatment in the current day. It is crucial to identify novel gastric ACPs to have an improved knowledge of their functioning processes and treatment of gastric cancer. As a result of the post-genomic era’s massive production of peptide sequences, rapid and effective ACPs using a computational method are essential. Several adaptive statistical techniques for distinguishing ACPs and non-ACPs have recently been developed. A variety of adapted statistically significant methods have been developed to differentiate between ACPs and non-ACPs. Despite significant progress, there is no specific model for the prediction of gastric ACPs because the specific model will predict a particular type of peptide more accurately and quickly. To overcome this, an initiative is taken for the creation of a reliable framework for the accurate identification of gastric ACPs. The current technique in particular contains four possible features along with one hybrid feature encoding mechanisms which are the target-class motif previously indicated by Amino Acid Composition, Dipeptide Composition, Tripeptide Composition (TPC), Pseudo Amino Acid Composition (PAAC), and their Hybrid. Machine Learning algorithms make high-performance and accurate prediction tools. Moreover, highly variable and ideal deep feature selection is done using an ANOVA-based F score for feature pruning. Experiments on a range of algorithms are carried out to identify the optimal operating strategy due to the diverse nature of learning. Following analysis of the empirical results, Naïve Bayes with TPC and Hybrid feature space outperforms other methods with 0.99 accuracy score on the testing dataset. To find the model generalization an external validation is carried out. In external datasets, the Extra Trees with PAAC features outperforms with the accuracy of 0.94. The comparison study shows that our suggested model will predict gastric ACPs more accurately and will be useful in drug development and gastric cancer. The predictive model can be freely accessed at https://github.com/humeraazad10/G-ACP.git. Communicated by Ramaswamy H. Sarma

Real-time operation strategy of virtual power plants with optimal power disaggregation among heterogeneous resources

The virtual power plant (VPP) can aggregate flexible resources on the demand side to provide frequency regulation for the grid, helping address the supply–demand balance challenges. When deploying regulation, the VPP disaggregates the requested power adjustment in real time among its internal heterogeneous resources. Achieving optimal power disaggregation in this process is challenging due to the temporal coupling characteristics of the resources, the uncertain regulation signals, and the requirement for fast response. Therefore, existing research relies on heuristic methods, such as proportional disaggregation, and fails to leverage the heterogeneity of multiple resources. Here, we propose an optimal operation strategy for VPPs to provide regulation, exploiting the complementary characteristics of heterogeneous resources by prioritising the use of low-cost resources while considering temporal coupling. To reduce the computational overhead of online deployment, we further propose a fast disaggregation algorithm to eliminate the reliance on optimisation solvers. We conducted case studies on the operation of a VPP composed of resources including thermostatically controlled loads and industrial production processes. The results verified the reduced operation cost and increased profit of the VPP under the proposed strategy, with only milliseconds of online computation time. We believe that our work can help better exploit demand-side flexibility.

Joint optimal operation of the South-to-North Water Diversion Project considering the evenness of water deficit

Abstract. Inter-basin water transfer projects are the main measure to address the water deficit crisis caused by uneven distribution of water resources. The current water transfer operation mainly tends to be present in areas with small water transfer costs and is prone to encounter the problem of spatial and temporal imbalances in water allocation. To address these issues, this paper defines a water deficit evenness index (WDEI), aimed at minimizing regional differences in water scarcity and sharing the pressure of water scarcity as a social demand objective. This index is incorporated into a joint optimization model for the Jiangsu section of the South-to-North Water Diversion Project (J-SNWDP), which comprises both the ecological objective of the total water deficit (TWD) and the economic objective of the pumping water (PW). Further, the Nondominated Sorting Genetic Algorithm III (NSGA-III) and multi-attribute decision-making were applied to solve the model and obtain an optimal operation strategy. The results showed the following: (1) the WDEI defined in this paper can mitigate the synchronized water scarcity in certain water users. In typical normal years (wet year and dry year), the WDEI shows a reduction of 94.2 % (81.8 %, 76.7 %) compared to the historical operation strategy. (2) The optimized operation strategy can significantly reduce TWD and PW by 82.06% (37.69 %, 52.36 %) and 45.13 % (3.25 %, 21.51 %) compared with the historical values, respectively, which can improve the water supply satisfaction and reduce the project cost. At the same time, the lake storage capacity of the optimal operation strategy performs well, and the water transfer efficiency of the river is significantly improved. (3) In this paper, targeted optimal operation strategies and potential ways to secure the project tasks are proposed for different natural flow. Overall, it is of great significance to study the water supply equity in the J-SNWDP to alleviate the concentrated water deficit in Jiangsu Province and other similar regions.

Operations and regulations for a ride-sourcing market with a mixed fleet of human drivers and autonomous vehicles

The rise of autonomous driving may sharply reshape ride-sourcing markets by influencing drivers’ income and participation, customers’ choices, ride-sourcing platforms’ operations, and governments’ regulations. We introduce a model that characterizes a ride-sourcing market with a mixed fleet of non-atomic autonomous vehicles (AVs) and atomic human-driven vehicles (HVs). Human drivers decide whether and where to offer ride-sourcing services independently, while AVs are fully controlled and dispatched by the platform to serve on-demand ride requests that are distributed heterogeneously. We solve for the optimal platform operating strategy and examine the implications of different government regulations on the platform’s optimal decisions and the resulting market equilibrium. Although the emergence of AV might undermine human drivers’ welfare, it can simultaneously increase platform profit, customer surplus, and social welfare. Moreover, by looking into the regulatory outcomes of a few prevailing regulations (i.e., price cap, minimum wage, minimum fleet size, and maximum fleet size), we find that the policymakers should determine the regulations according to the conditions of labor supply and the development stage of AV technology.

Novel data-driven optimal control methods for cost-effective brine treatment

This study presents a novel data-driven optimal control method to minimize the cost of Convection-Enhanced Evaporation (CEE) systems under time-varying weather conditions. CEE is the approach of evaporating water from saline films (brine) on packed evaporation surfaces by air convection. Here, operating variables (brine injection rate, brine temperature, and air speed) are actively controlled as a function of current ambient conditions and daily weather forecast. The controller optimizes the process operation variables based on a dataset consisting of Pareto fronts, obtained in advance by solving a set of optimization problems. Three optimal operation strategies are presented: (1) real-time selection of operating variables, (2) predictive scheduled operation, and (3) hybrid wind-fan operation. The effectiveness of the proposed strategies was assessed through two case studies with distinct geographical locations and weather conditions: Alamogordo, New Mexico, and Minneapolis, Minnesota. The results show significant cost-saving potential relative to static operation. Predictive scheduled operation resulted in an annual average operating costs of $0.91/m3 and $6.63/m3 in Alamogordo and Minneapolis, respectively, with the higher costs in Minneapolis a result of the added thermal energy required to prevent freezing in winter.

Enhancing efficiency in electrical distribution systems: A novel approach via modified genetic optimization algorithm for loss reduction in optimal network distribution

The distribution system plays a pivotal role in connecting power generation sources to vital facilities like nuclear reactors. In this intricate network, losses occur while supplying electricity, demanding a reduction for enhanced performance. The quality of power reaching the nuclear plant is imperative due to the susceptibility of sensitive equipment to poor power conditions. This study presents a reconfiguration strategy to bolster dependability and curtail power losses in distribution networks. Leveraging the Modified Genetic Optimization Algorithm (MGOA), the reconfiguration conundrum is tactfully addressed to determine optimal switch operation schemes. The MGOA-based reconfiguration not only minimizes energy wastage but also refines voltage profiles, elevating operational efficiency. The effectiveness of this approach is substantiated through its successful application to radial distribution systems comprising 33, 69, and 136 buses. Embracing diverse scenarios encompassing normal and abnormal operating states, as well as varying loads, the method’s robustness is showcased. The validity of the proposed methodology is reinforced by comprehensive simulation results, underscoring its reliability and potential for real-world implementation.

Electric Taxi Charging Load Prediction Based on Trajectory Data and Reinforcement Learning—A Case Study of Shenzhen Municipality

In order to effectively solve the problem of electric taxi charging load prediction and reasonable charging behaviour discrimination, in this paper, we use taxi GPS trajectory data to mine the probability of operation behaviour in each area of the city, simulate the operation behaviour of a day by combining it with reinforcement learning ideas, obtain the optimal operation strategy through training, and count the spatial and temporal distributions and power values at the time of charging decision making, so as to predict the charging load of electric taxis. Experiments are carried out using taxi travel data in Shenzhen city centre. The results show that, in terms of taxi operation behaviour, the operation behaviour optimized by the DQN algorithm shows the optimal effect in terms of the passenger carrying time, mileage, and daily net income; in terms of the charging load distribution, the spatial charging demand of electric taxis in each area shows obvious differences, and the charging demand load located in the city centre area and close to the traffic hub is higher. In time, the peak charging demand is distributed around 3:00 to 4:00 and 14:00 to 15:00. Compared with the operating habits of drivers based on the Monte Carlo simulation, the DQN algorithm is able to optimise the efficiency and profitability of taxi drivers, which is more in line with the actual operating habits of drivers formed through accumulated experience, thus achieving a more accurate charging load distribution.

A comprehensive technical analysis on optimal sizing and operating strategy for large-scale direct coupled PV–electrolyser systems, considering PV system faults, degradation and partial shading conditions

The paper presents an investigation of the performance of the proton exchange membrane (PEM) electrolyser directly connected with large-scale PV systems under faulty, degradation and partial shading conditions. With the direct coupled system, the power loss and external price on auxiliary devices are avoided. However, the adaptability of the direct coupled system to fluctuate solar irradiance and ambient temperature is reduced significantly compared to systems with DC-DC converters. To maximise the system efficiency, the optimal electrolyser (ELY) cell configuration is identified using an optimal sizing approach or operating strategy. This paper applied the Particle Swarm Optimisation (PSO) algorithm to find the optimal configuration for the two approaches, namely the Sizing technique and Operating strategy under different PV systems: faults, degradation and partial shading conditions. Additionally, a large-scale system is simulated to describe the characteristics of a large-scale direct coupled PV-electrolyser system with actual operating faults and phenomena. Simulations of the large-scale direct coupling system were conducted using measured irradiance and temperature data for a typical day period. Based on the collected output, it is found that compared to the optimal sizing approach, the optimal operating strategy allows the direct coupled system to increase its coupling efficiency from 0.17% to 4.47%. In contrast, the amount of hydrogen production when applying the two methods witnessed different trends, depending on the type of faults. The differences are even more significantly presented during multiple short-circuit fault scenarios.

Optimal Operation Strategy of Electric Vehicle Cluster in the Electricity Spot Market Considering Scheduling Capability

The widespread use of electric vehicles (EV) has put a strain on the stable operation of power grid. Therefore, the potential of EV cluster power load regulation has been paid attention. In the cluster, the electric vehicle aggregator (EVA) can gather a large number of EVs and participate in the electricity spot market by optimizing the charging/discharging power. In this study, a bi-objective optimization model for V2G enabled EV cluster operation is proposed to determine the optimal load of EV cluster considering the electricity spot market. First, the scheduling capability of EVs is modelled and aggregated considering the EV user willingness. Then, the demand response and electricity spot trade for EVA are analyzed. Based on the capability constraints and the market rules, an optimization model is established with two objectives of maximizing EVA profits and EV user satisfaction. Finally, a case study in Beijing, China is implemented to prove the feasibility of the proposed model. The results show that the EV user willingness for orderly charging/discharging is distributed in the range of 0.26 and 0.94 with an average value of 0.85. In addition, the proposed EV cluster operation strategy can improve the EVA daily profits by 81.27% and increase the EV user satisfaction by 70% compared with normal charging strategy.

A Three-Stage Optimal Operation Strategy of Interconnected Microgrids With Rule-Based Deep Deterministic Policy Gradient Algorithm.

The ever-increasing requirements of demand response dynamics, competition among different stakeholders, and information privacy protection intensify the challenge of the optimal operation of microgrids. To tackle the above problems, this article proposes a three-stage optimization strategy with a deep reinforcement learning (DRL)-based distributed privacy optimization. In the upper layer of the model, the rule-based deep deterministic policy gradient (DDPG) algorithm is proposed to optimize the load migration problem with demand response, which enhances dynamic characteristics with the interaction between electricity prices and consumer behavior. Due to the competition among different stakeholders and the information privacy requirement in the middle layer of the model, a potential game-based distributed privacy optimization algorithm is improved to seek Nash equilibriums (NEs) with encoded exchange information by a distributed privacy-preserving optimization algorithm, which can ensure the convergence as well as protect privacy information of each stakeholder. In the lower layer of the model of each stakeholder, economic cost and emission rate are both taken as operation objectives, and a gradient descent-based multiobjective optimization method is employed to approach this objective. The simulation results confirm that the proposed three-stage optimization strategy can be a viable and efficient way for the optimal operation of microgrids.

Environmental/Economic Power Dispatch of MicroGrid Using Multiobjective Optimization

This paper proposes a generalized formulation to determine the optimal operating strategy and cost optimization scheme as well as the reduction of the emissions for a MicroGrid (MG). Multiobjective (MO) optimization is applied to the environmental/economic problem of the MG. The proposed problem is formulated as a nonlinear constrained MO optimization problem. Prior to the optimization, models for system components from real data are constructed. The problem formulation takes into consideration the operation and maintenance costs as well as the emissions NOx, SO2, and CO2 reduction. The MG considered in this paper consists of a wind turbine, a micro turbine, a diesel generator, a photovoltaic array, a fuel cell, and a battery storage. The optimization is aimed at minimizing the cost function of the system while constraining it to meet the costumer demand and safety of the system. We also add a daily income and outgo from sale or purchased power. The results demonstrate the efficiency of the proposed approach to satisfy the load and to reduce the cost and the emissions. The comparison with other techniques demonstrates the superiority of the proposed approach and confirms its potential to solve the problem.

Research on Grid-Connected Optimal Operation Mode between Renewable Energy Cluster and Shared Energy Storage on Power Supply Side

The renewable energy cluster can reduce the total power deviation of renewable energy stations and also bring cooperative benefits to renewable energy stations. Shared energy storage can assist in tracking the power generation plan of renewable energy and has advantages in the scale of investment, utilization rate, and other aspects. Therefore, this article proposes a study on the grid-connected optimal operation mode between renewable energy cluster and shared energy storage on the power supply side. Firstly, for the complementary characteristics blurring each member’s contribution to the cluster power deviation, an improved Shapley value method is used to build a rational mechanism for apportioning the cluster power deviation penalty. Secondly, based on the Nash negotiation model and allocation mechanism, the revenue model of the renewable energy cluster is constructed, and an improved particle swarm optimization algorithm is proposed to solve the cluster operation strategy. Then, based on the charge and discharge demand of renewable energy cluster, with the goal of maximizing the benefits of shared energy storage, capacity pricing is optimized, as well as capacity allocation and participation in the frequency control auxiliary service market. Finally, the solving process of grid-connected optimal operation mode is proposed, and the rationality of the grid-connected optimal operation strategy between renewable energy cluster and shared energy storage is verified by example analysis. The results indicate that renewable energy cluster and shared energy storage can effectively increase both benefits, and a win-win situation for all parties can be realized. On the one hand, the cooperation mode and allocation mechanism can effectively guarantee the benefit of each renewable energy station. On the other hand, shared energy storage is beneficial for assisting in tracking the power generation plan of renewable energy and reducing the renewable energy power curtailment situation. There also has residual capacity in the shared energy storage system; it can participate in the frequency control ancillary service market, which can improve the capacity utilization rate and benefit of shared energy storage.

Design and operational strategy optimization of a hybrid electric heating system with phase change materials for energy storage in nearly zero energy buildings

Nearly zero energy buildings (nZEBs) and the associated research on heating energy systems are gaining increasing attention. To enhance PV self-consumption capacity in nZEBs, a hybrid electric heating system with phase change materials (PCM) for energy storage using photovoltaic (PV) and grid power was developed. To study the system's performance, an experimental bench was set up, and mathematical models for energy efficiency and operational strategy were developed. Furthermore, a research building was chosen as the scenario to investigate the operational performance of the system. The analysis focused on assessing the impact of varying PV and battery capacity, specifically concentrating on load flexibility transfer capability and energy flow within the system. The results indicated that when implementing the hybrid electric heating system with varying PV and battery capacities, significant transfers of both heat and electricity loads could be achieved, leading to peak price electricity usage remaining below 10 %. Regardless of the manner in which PV is utilized, the system facilitates the transfer of both heat and electricity loads, resulting in peak price electricity usage remaining below 5 %. Through an analysis involving the optimization of system capacity and operational strategies during a simulated one-week period in the heating season, the results revealed that the utilization of a battery with a rated capacity as low as 3.69 kWh led to an increase of 10.93 kWh in PV self-consumption, resulting in a noteworthy cost reduction of 15.6 %.