Regional Integrated Energy System Research Articles

Short-term load forecasting of regional integrated energy system based on spatio-temporal convolutional graph neural network

Accurate short-term load forecasting has significant reference value for the preliminary planning and subsequent coordinated control of regional integrated energy systems. However, main limitations are: (1) Insufficient exploration of the complex correlations among diverse loads and between loads and meteorological factors; and (2) Feature information extraction only from the time dimension, neglecting the spatial dimension, resulting in incomplete feature information extraction. This paper proposes a new method for short-term load forecasting, namely the Attention Spatial–temporal Graph Convolutional Network (ATSTGCN) method. Firstly, the Grey Relational Analysis (GRA) method is used to analyze the correlation between diverse loads, meteorological features, and load features, constructing the input data for the graph neural network. Then, synchronous graph convolution is introduced to extract information between features. Next, the spatio-temporal attention mechanism is incorporated into the synchronous graph convolution to capture spatio-temporal features and reduce information loss using a spatio-temporal window. Finally, a short-term load forecasting method based on ATSTGCN for regional integrated energy systems is constructed. Extensive simulation experiments are conducted on the publicly available dataset from Arizona State University’s online system. The experimental results show that compared to other methods, the proposed method improves MAPE, MAE, and RMSE by 5.6%, 11.7%, and 11.6%.

Research on station-network planning of electricity-thermal -cooling regional integrated energy system considering multiple-load clusters and network costs

A reasonable regional integrated energy system (RIES) structure is the key to achieving its economic and efficient operation. However, in actual engineering construction, only the maximum load is considered as a prerequisite for energy design, which leads to waste of energy allocation. In view of this, this article proposes an electricity-thermal-cooling RIES energy stations and networks planning model that coordinates the operation of multiple-user loads, energy networks, and energy stations based on the idea of load clustering. Firstly, an average peak valley difference rate covering electricity, thermal, and cooling loads was proposed to evaluate the load characteristics of the RIES. Secondly, based on the load cluster characteristics and network costs of RIES, a RIES planning optimization framework is proposed, which includes load cluster division, RIES site selection and network planning, and RIES capacity planning. Thirdly, based on the P-median model and load cluster characteristics, establish an RIES location and network layout model. Fourthly, establish an RIES capacity planning model with the goal of annual total cost and carbon emissions. Finally, Dijkstra, improved particle swarm optimization, and VIKOR were used to solve the model. The results showed that the proposed model reduced the annual total cost by 7.13 % and the annual carbon emissions by 3.72 %.

Load forecasting for regional integrated energy system based on two-phase decomposition and mixture prediction model

Current load forecasting methods struggle with the randomness and complexity of multiple loads in regional integrated energy systems, resulting in less accurate predictions. To address this problem, this paper proposes a novel two-phase decomposition hybrid forecasting model that combines complementary ensemble empirical mode decomposition, sample entropy, variational mode decomposition, and genetic algorithm-bidirectional long short term memory. The proposed approach begins by decomposing the load sequence into multiple intrinsic mode function components at different frequencies using complementary ensemble empirical mode decomposition. Afterward, the sample entropy values are calculated for each intrinsic mode function. The intrinsic mode function with the highest sample entropy value is subjected to a two-stage decomposition using the variational mode decomposition method. Through this technique, a series of stationary components is acquired. Subsequently, the bidirectional long short term memory model is optimized using the genetic algorithm, and the genetic algorithm-bidirectional long short term memory approach is employed to predict all the decomposed components. Finally, the prediction results are combined and reconstructed to obtain the final prediction value. Experimental results demonstrate the effective handling of non-stationary load sequences by the forecasting model, showcasing the highest level of accuracy in regional integrated energy system multiple loads forecasting.

Optimization Operation Strategy for Shared Energy Storage and Regional Integrated Energy Systems Based on Multi-Level Game

Regional Integrated Energy Systems (RIESs) and Shared Energy Storage Systems (SESSs) have significant advantages in improving energy utilization efficiency. However, establishing a coordinated optimization strategy between RIESs and SESSs is an urgent problem to be solved. This paper constructs an operational framework for RIESs considering the participation of SESSs. It analyzes the game relationships between various entities based on the dual role of energy storage stations as both energy consumers and suppliers, and it establishes optimization models for each stakeholder. Finally, the improved Differential Evolution Algorithm (JADE) combined with the Gurobi solver is employed on the MATLAB 2021a platform to solve the cases, verifying that the proposed strategy can enhance the investment willingness of energy storage developers, balance the interests among the Integrated Energy Operator (IEO), Energy Storage Operator (ESO) and the user, and improve the overall economic efficiency of RIESs.

Multi-objective optimization study of regional integrated energy systems coupled with renewable energy, energy storage, and inter-station energy sharing

Globally, countries have established timelines and technological pathways towards achieving "carbon neutrality". Regional integrated energy systems, as an efficient and clean mode of energy provision, are particularly suitable for supplying various forms of energy to building users. However, due to the complexity of their structure and multiple energy flow couplings, regional integrated energy systems face challenges in system optimization, warranting further exploration in design optimization and benefit analysis. Therefore, a regional integrated energy system was established, integrating renewable energy, energy storage, and power/thermal sharing between stations. A multi-objective optimization model for the regional integrated energy system was established, targeting economic benefits, carbon reduction, and reliability. Overall benefits of the internal energy stations in the regional integrated energy system were meticulously analyzed, considering system benefits, inter-station energy sharing, and energy storage. Research findings indicate, the regional integrated energy system constructed in this study exhibited superior energy-saving, carbon reduction, and independence, compared to other integrated energy systems. For the proposed system, the annual total cost was 38.41 CNY/m2, primary energy consumption stood at 16.26 kWh/m2, carbon emission was recorded at 4.33 kg/m2, and the system interaction power measured 9.99 kWh/m2. Final, this study provides theoretical support for the application and promotion of regional integrated energy systems.

A novel dynamic simulation strategy for regional integrated energy system considering coupling components failure

Dynamic simulation of regional integrated energy systems (RIES) is beneficial for improving stability and safety of RIES. However, it is hard to implement a dynamic simulation due to the RIES's complexity caused by the coupled multiple energy sources, especially considering coupling components failure. To address this issue, a novel fault-driven co-simulation strategy for coupled heat-gas-electric system under different states is developed in this study. This strategy includes two steps: (1) the RIES is firstly decomposed into different modules and construct corresponding sub-models; and then (2) the solution sequence of these sub-models is determined according to the system states. Additionally, the strategy also considers the transmission delay of gas and heat. Two typical components failures (combined heat and power unit failure and gas-fired generator unit failure) at a typical industrial park's RIES are selected to validate the dynamic simulation strategy. And results indicate that such strategy can obtain accurate dynamic behavior of RIES after coupling components failures occurs, such as the flow fluctuation of the gas pipeline that lasts for 179 s and the temperature reduction process of the heat node that lasts for more than 35000 s. Therefore, this study is instructive for further development of RIES technology.

Interactive scheduling optimization of regional multi-agent integrated energy systems considering uncertainties based on game theory

With the increasing number of integrated energy system (IES) construction, a multi-agent system group composed of multiple IESs would be formed in a certain region. Moreover, complex uncertainties gradually existed in the IES which affect the system optimal situation and optimization results. In order to take full use of each agent's advantages and improve the performance of the whole region under uncertainty, this paper focuses on the multiple energy types' interaction of multi-agent IESs within the region to realize the stable operation, economic and environmental improvement of each agent and the whole region. To this aim, a multi-objective multiple energy types' multi-agent interactive optimization model considering energy demand and renewable energy output uncertainties was built. The model was based on Nash bargaining cooperative game theory and took economy, flexibility and carbon emission as objectives. Moreover, energy interaction scheduling and its corresponding price can be optimized through the model. The results of non-cooperative operation mode without interaction and cooperative operation mode with interaction were compared to analyze the superiority of interaction from the cost, carbon emission and flexibility aspects. The overall regional carbon emission and the economic cost has been reduced by 13.84% and 9.94%, for each IES, the carbon emission and economic cost could reduce by up to 41.5% and 44.5%. Moreover, the flexibility of the IES was correspondingly increased by nearly 47%. These results denoted that after multi-agent interaction, not only the overall region's energy, economic and environmental performance are improved, but also that of each agent are significantly improved. In addition, different energy types among multiple agents could be effectively used and regional joint energy storage is realized, improving the region flexibility level and resilience.

Multi-agent low-carbon optimal dispatch of regional integrated energy system based on mixed game theory

In the context of global warming and energy scarcity, regional integrated energy system (RIES) can effectively improve energy utilization efficiency and reduce carbon emissions. However, this leads to the increased complexity of the market transactions. To this end, based on the mixed game strategy, a multi-agent low-carbon optimal dispatch model of RIES is proposed in this paper. Firstly, to fully consider the low-carbon objective of the system, a reward and punishment ladder carbon trading mechanism and a ladder integrated demand response (IDR) model are developed. Then, the decision models of the energy management operator (EMO), multiple energy hub agents (EHAs) and the user load aggregator (ULA) are constructed respectively with the goal of economy, and carbon emission flows are introduced as constraints. The interactive trading process of various stakeholders is simulated by a mixed game with master-slave game nested bidding strategy. Finally, the economics and carbon emissions of the system in different scenarios are analyzed by examples, and the effectiveness of the proposed model is verified. Numerical simulation results show that the proposed method not only has good economic and low-carbon environmental protection benefits, but also ensures the interests of various stakeholders.

Multi-agent system consistency-based cooperative scheduling strategy of regional integrated energy system

This article presents a multi-agent system (MAS)-consistency-based cooperative scheduling strategy for regional integrated energy system to address the real-time economic scheduling problem with high penetration of multiple flexible loads. First, the MAS-based hierarchical optimal operation framework is developed to perform the real-time scheduling, where the framework with distributed control is further developed into the hierarchical framework with low-dimensional adjacency matrix. Furthermore, the economic scheduling optimization models corresponding to hierarchical distributed control mode and distributed control mode are established. The optimal objective of the proposed control structure is to 1) minimize the total operation cost, 2) optimize the electrical and heat power of all dispatchable units in balancing energy supply-demand. The operational rule of following heat load for hierarchical distributed control mode and a hill-climbing search model for distributed control mode are proposed to achieve the consistency of agents, so as to realize the cooperative optimal dispatch of electricity and heat vectors. Simulation results validate the effectiveness of the proposed MAS-based strategy in optimizing the cooperative operation of electricity-heat vectors. The real-time energy generation and load demand can be accurately balanced. The proposed strategy can perform efficient real-time economic scheduling and has satisfied adaptive capability for random variations of flexible loads.

A low-carbon economic dispatch method for regional integrated energy system based on multi-objective chaotic artificial hummingbird algorithm

This paper investigates Regional Integrated Energy Systems (RIES), emphasizing the connection of diverse energy supply subsystems to address varied user needs and enhance operational efficiency. A novel low-carbon economic dispatch method, utilizing the multi-objective chaotic artificial hummingbird algorithm, is introduced. The method not only optimizes economic and environmental benefits but also aligns with "carbon peak and carbon neutrality" objectives. The study begins by presenting a comprehensive low-carbon economic dispatch model, followed by the proposal of the multi-objective chaotic artificial hummingbird algorithm, crucial for deriving the Pareto frontier of the low-carbon economic dispatch model. Additionally, we introduce a TOPSIS approach based on combined subjective and objective weights, this approach harnesses the objective data from the Pareto solution set deftly, curbs the subjective biases of dispatchers effectively and facilitates the selection of an optimal system operation plan from the Pareto frontier. Finally, the simulation results highlight the outstanding performance of our method in terms of optimization outcomes, convergence efficiency, and solution diversity. Noteworthy among these results is an 8.8% decrease in system operational economic costs and a 14.2% reduction in carbon emissions.

Optimal scheduling of integrated energy system using decoupled distributed CSO with opposition-based learning and neighborhood re-dispatch strategy

Integrated energy optimization scheduling (IEOS) is a complex problem aiming to minimize the total cost while the requirements of load balance is met. Due to the non-convex, non-differentiable and high-dimensional characteristics, there are many difficulties in solving the problem. Based on a regional integrated energy system (RIES), a decoupled distributed crisscross optimization with opposition-based learning and neighborhood re-dispatch strategy (DDCSO-OBL-NR) is proposed to solve IEOS problem by distributed method with different energy types as the scale. Initially, the CSO with excellent global search ability is firstly used to solve the complicated IEOS problem. Then, based on the distributed structure, distributed parallel computing can be achieved by DDCSO, which contributes to 1) protect the privacy of different energy data, 2) reduce the solving dimensions and 3) relieve the heavy communication burden. The total optimal cost is achieved by minimizing the cost of each portion without centralized controller. Furthermore, the opposition-based learning (OBL) strategy and the neighborhood re-dispatch (NR) strategy are combined into DDCSO aiming to optimize initial population location and enhance local search ability. Eventually, the DDCSO-OBL-NR is realized, and the effectiveness of which in solving the distributed IEOS problems is verified by the experimental results of three cases.

Optimal power flow calculation method for regional integrated energy system based on double-layer optimization

In this paper, a two-layer optimization calculation method is proposed to improve the practical application efficiency of the integrated energy system and to improve the different problems of power flow calculation. Firstly, the power flow model is constructed and the optimization function is established based on indicators such as economy and environmental protection. The two-layer optimization method is proposed by combining the quadratic programming method and the particle swarm algorithm. Then, the obtained operating conditions of each energy equipment are used as the input of the power flow. The processing of each device and the energy flow of the power flow network are calculated and the effectiveness of the algorithm is verified by MATLAB power analysis.

Study on optimal allocation of energy storage in multi-regional integrated energy system considering stepped carbon trading

Abstract In this study, an energy storage configuration optimization model of multi regional integrated energy system based on integrated scheduling and stepped Carbon emission trading is proposed. By analyzing the steady-state, dynamic, and variable operating characteristics of multi regional integrated energy systems, a distributed energy planning modeling method was adopted to construct energy storage configuration constraint objects and optimization models. The target scheme of energy storage configuration is optimized by using the results of integrated scheduling scheme and dynamic distribution analysis of ladder Carbon emission trading, and the parameters are optimally estimated by using Monte Carlo method and quadratic fitting algorithm. The simulation results show that this method has a strong scheduling capability in the context of stepped Carbon emission trading, can accurately assess the load demand and supply demand relationship, reduce carbon emissions, and improve energy efficiency, with an average of 0.9121.

Operational Optimization of Regional Integrated Energy Systems with Heat Pumps and Hydrogen Renewable Energy under Integrated Demand Response

A regional integrated energy system (RIES), synergizing multiple energy forms, is pivotal for enhancing renewable energy use and mitigating the greenhouse effect. Considering that the equipment of the current regional comprehensive energy system is relatively simple, there is a coupling relationship linking power generation, refrigeration, and heating in the cogeneration system, which is complex and cannot directly meet various load demands. This article proposes a RIES optimization model for bottom-source heat pumps and hydrogen storage systems in the context of comprehensive demand response. First, P2G electric hydrogen production technology was introduced into RIES to give full play to the high efficiency advantages of hydrogen energy storage system, and the adjustable thermoelectric ratio of the HFC was considered. The HFC could adjust its own thermoelectric ratio according to the system load and unit output. Second, through the ground-source heat pump’s cleaning efficiency function, further separation and cooling could be achieved. The heat and electrical output of RIES improved the operating efficiency of the system. Thirdly, a comprehensive demand response model for heating, cooling, and electricity was established to enable users to reasonably adjust their own energy use strategies to promote the rational distribution of energy in the system. The model integrates power-to-gas (P2G) technology, leveraging the tunable thermoelectric ratio of a hydrogen fuel cell (HFC) to optimize the generation of electricity and heat while maximizing the efficiency of the hydrogen storage system. Empirical analysis substantiated the proposed RIES model’s effectiveness and economic benefits when integrating ground-source HP and electric hydrogen production with IDR. Compared with the original model, the daily operating cost of the proposed model was reduced by RMB 1884.16.

Research on collaborative scheduling of internet data center and regional integrated energy system based on electricity-heat-water coupling

Aiming at the carbon peaking and carbon neutrality goals, the water resource and energy management of internet data center involves many aspects, including the interaction among internet data center, power grid, water network and heat consumer. As an important demand response resource for the power grid, the interaction between both can improve the flexibility of the power grid, and ensure the power supply reliability and operation economy of internet data center. Existing references assume that regional integrated energy system can obtain all data information and scheduling permissions of internet data center, ignore the autonomous decision-making ability of both, resulting in its application has some constraints. The operation pattern switchover of internet data center and data allocation among multiple internet data center s are ignored, it is not possible to fully play the flexibility characteristics of internet data center. The study of interaction between internet data center and power grid simplifies the power grid as an ideal power source, ignoring its operational constraints. Internet data center configured with renewable energy, electric energy storage device and water-cooling system is taken as one of the research objects in this paper, and regional integrated energy system formed by the coupling of distribution network and water network as the other. The water-energy coupling and cooperative operation between above two are studied for solving the above problem. The proposed method has been validated in the demonstration project of the “East Data and West Computing” big data industrial park in Qingyang, Gansu, China. The results of example show that water-energy demand response and waste heat reuse can help to reduce the operational cost of internet data center, improve scheduling flexibility of regional integrated energy system, and enhance the capacity of renewable energy utilization. The operational cost of internet data center and regional integrated energy system is sensitive to emergency capacity of electric energy storage device, cost coefficient of the power demand response of electric energy storage device and water demand response of reservoir, data load flexible scheduling strategy, which is consistent with the operational status of the demonstration project.

Multi-objective station-network synergy planning for regional integrated energy system considering energy cascade utilization and uncertainty

Regional integrated energy system (RIES) is conductive to integrating distributed renewable energy and represent a crucial form for constructing low-carbon energy systems in future cities. Addressing key issues in RIES planning such as station-network synergy optimization, uncertainty optimization, and multi-objective optimization, this paper proposes a synergy planning framework for RIES considering energy cascading utilization and uncertainty. Firstly, a dual-layer synergy planning model is established. In the upper layer, it resolves the station-network layout optimization problem considering user distribution and load complementarity. An improved P-median model is introduced to optimize the site selection and network layout of energy stations. In the lower layer, a two-stage stochastic programming model is proposed to address the configuration optimization problem for energy stations considering uncertainty. To enhance the energy utilization efficiency of RIES, exergy efficiency is incorporated into the multi-objective optimization model. A combination of ε-constrant methods and the TOPSIS method is employed for multi-objective solving and decision-making. The proposed models and methods are validated through a case study. The analysis of seven comparative experiments indicates that there is an optimal number of energy stations for the overall economic efficiency in RIES station-network layout optimization. Compared to single-objective optimization, multi-objective optimization considering both economic and exergy efficiency has advantages in improving energy utilization efficiency and reducing carbon emissions. When the load complementarity between multiple regions is higher, the interconnection gains between energy stations are greater. The research framework proposed in this study can provide essential references for designers of urban RIES.

Multi-objective optimization of regional integrated energy system matrix modeling considering exergy analysis and user satisfaction

Integrated energy system achieves the collaborative optimization of different energy forms such as electricity/gas/heat/cold, and it is an important strategic development direction in the international energy field in the future. However, with the development of smart grid technology and the increase of renewable energy penetration, it needs more flexible load to adapt to the changing energy supply–demand relationship. Considering the impact of user-side load fluctuations on the system, this paper establishes a two-tier closed-loop system model formed by users and the market. The upper-layer is based on the user’s perspective. A comprehensive user satisfaction model is established by the radar chart area method and consumer preference theory. The lower-layer is based on the market perspective. It proposes an improved layering matrix Energy Hub model and conducts the regional integrated energy system exergy efficiency evaluation study. Then, taking economy, carbon emission and exergy efficiency as objective functions, a multi-objective optimization model is constructed. Non-dominated Sorting Genetic Algorithms-II is applied to solve the model, and the output parameters is a Pareto front surface. A combination weighting TOPSIS method is used to select a compromise solution of multi-objective optimization from the optimal solution set. Compared with the primary energy utilization efficiency as the objective function, the exergy efficiency reduces the system cost by 8.64%. Finally, the multi-objective optimization solution based on different user satisfaction is obtained. Through the analysis of the examples, compared with the original load, the introduction of the upper-layer user satisfaction model reduces the system cost by 2.8%. The reasonable setting of user satisfaction and load adjustment amount can improve the economy of system, which verifies the effectiveness of the two-tier closed-loop system based on user satisfaction model.

Coordinated optimization design of buildings and regional integrated energy systems based on load prediction in future climate conditions

The influence of global warming on building performance necessitates its consideration during the design phase. The conventional approach of separately designing the buildings and energy systems may not yield the globally optimal solution. To address this issue, this study proposes a coordinated optimization design approach for buildings and regional integrated energy systems (RIES) under future climate conditions. Firstly, a future regional load prediction model based on a Random Forest algorithm optimized by the Grey Wolf Optimizer is established. Next, a bi-level optimization model is proposed, with the upper level optimizing the building envelope to minimize costs and carbon emissions, and the lower level determining the optimal configuration capacity and operating strategy of RIES for cost minimization. Multi-objective grey wolf optimizer and CPLXE are employed to address the problem. Finally, the proposed method is validated using a case study of an office area in Jinan. The results show that the method can reduce costs by 14.6 % and carbon emissions by 50.93 % compared to the prototype building area with independent production systems, highlighting the potential of synergistic design between buildings and RIES. In addition, different optimization results were obtained under typical meteorological year, shared socioeconomic pathway (SSP) 245, and SSP585 climate scenarios, indicating the impact of future climate on building and RIES design. Therefore, it is necessary to consider climate change in future design work.

Stackelberg-Nash asymmetric bargaining-based scheduling optimization and revenue-allocation for multi-operator regional integrated energy system considering competition-cooperation relationship and source-load uncertainties

The integration of source-grid-load-storage greatly improves the energy managing flexibility of regional integrated energy systems (RIES). Considering the conflicting interests of multi-operator, this paper divides RIES into 1) energy supply center (ESC); 2) load aggregator (LA); 3) energy storage aggregator (ESA), and establishes a multi-operator two-layer optimal scheduling model by combining 1) the Stackelbelberg-Nash asymmetric bargaining game based on the genetic algorithm; and 2) the alternating direction method of multipliers algorithm (GA-ADMM). Firstly, considering the absence of historical data, trapezoidal fuzzy parameters are introduced to construct fuzzy chance constraints based on credibility theory to simulate the source-load uncertainties. Secondly, the uncertainty of demand response behavior, real-time carbon credits trading within RIES, and cooperation-competition behavior of followers (LA and ESA) are considered to simulate the asymmetric relationship between followers and leaders (ESC) based on GA. Then, based on the ADMM algorithm, a Nash asymmetric bargaining game considering energy dependency and energy supply-reception status is proposed to realize the revenue allocation between followers. Finally, simulations are implemented to verify the validity and reliability of the proposed model. Results show that: 1) Cooperation improves the energy-managing flexibility of followers, reduces their dependence on ESC, and thus enhances their game ability. 2) The real-time carbon credits trading enables zero-carbon energy interactions and natural gas replenishment within RIES. 3) Increases in the price of various energy and carbon credits can enhance cooperation between followers.

Multi-agent game operation of regional integrated energy system based on carbon emission flow

In the process of promoting energy green transformation, the optimization of regional integrated energy system faces many challenges such as cooperative management, energy saving and emission reduction, as well as uncertainty of new energy output. Therefore, this paper proposes a multi-agent game operation method of regional integrated energy system based on carbon emission flow. First, this paper establishes a carbon emission flow calculation model for each subject, and proposes a comprehensive tariff model based on the carbon emission flow, which discounts the carbon emissions from the power supply side to the power consumption side. Secondly, considering the interests of each subject, this paper establishes the decision-making model of each subject. And the new energy uncertainty, the cost of energy preference of prosumers, and the thermal inertia of buildings are considered in the decision model. Finally, the model is solved using differential evolution algorithm and solver. The case study verifies that the comprehensive electricity pricing model based on carbon emission flow developed in this paper can play a role in balancing economy and low carbon.