Regional Integrated Energy System Research Articles

Coordination for regional integrated energy system through target cascade optimization

Coordination for regional integrated energy system through target cascade optimization

Hierarchical optimal scheduling method for regional integrated energy systems considering electricity-hydrogen shared energy

Shared energy storage is widely recognized as an energy hub for the coordinated operation of regional integrated energy systems (RIESs). Multi-energy systems (MESs) share centralized energy storage to store excess renewable energy sources (RESs). This paper proposes a novel energy-sharing framework considering hydrogen trading, which is suitable for residential, industrial, and commercial RIES structures. Shared energy storage operator (SESO) promotes hydrogen energy transactions by formulating time-of-use (TOU) hydrogen prices. The proposed hydrogen energy trading method can be regarded as a master-client structure, and a hierarchical optimal scheduling method based on the Stackelberg game relationship is designed. Finally, a case study is performed based on an optimal scheduling algorithm combining particle swarm optimization (PSO) and mixed integer linear programming (MILP). The cases show the electricity‑hydrogen shared energy storage mechanism in RIESs can improve the RESs utilization rate and effectively reduce the operating costs of each system. Moreover, compared with RIESs with a single centralized electric energy storage, the TOU hydrogen price mechanism can further lower the energy prices and improve the economy of the RIES. The cases show that hydrogen energy sharing reduces the operating cost of RIES by 9.96% and increases the regional energy utilization rate by 2.97%. And the shared energy storage business model can satisfy the economy of both SESO and RIES. It confirms the rationality of the electric hydrogen shared energy storage project.

A data-based day-ahead scheduling optimization approach for regional integrated energy systems with varying operating conditions

Two of the most critical issues encountered in the day-ahead scheduling of regional integrated energy systems are the uncertainty of renewable energy resources and complexity of load demand. Furthermore, varying operating conditions also pose challenges for economical day-ahead scheduling. This paper proposes a scenario-based day-ahead scheduling approach for regional integrated energy systems to minimize operating costs by mining historical data. A hybrid dynamic energy hub model with variable efficiency that integrates an extreme gradient boosting (XGBoost) algorithm and analytical formulation is proposed. We developed a scenario-based scheduling optimization model in which climate data and load data are predicted using XGBoost and the probability distributions of their predicted errors are estimated using a Gaussian mixture model. Monte Carlo simulation and K-means clustering were used to generate and reduce scenarios and a success-history-based adaptive differential evolution algorithm was adopted to search for optimal solutions for day-ahead scheduling. Furthermore, a weighted average electricity purchasing strategy was adopted to address uncertainty and further improve operating economy by adjusting the output of gas turbines and electricity purchasing for actual scheduling. Case studies were conducted to verify that the proposed approach can reduce daily operating costs and enhance the operating economy of regional integrated energy systems.

Day-ahead bidding strategy of regional integrated energy systems considering multiple uncertainties in electricity markets

The regional integrated energy system (IES) is considered a promising paradigm for economic multi-energy provision while promoting the accommodation of renewable distributed energy resources (RDER). However, various uncertainties faced by the IES pose challenges to fulfilling its functionalities and economic promises. This paper proposes a day-ahead optimal bidding strategy of IES in electricity markets with multiple time scales of electricity settlement and energy dispatch for minimizing its expected operational cost based on the hybrid scenario-based stochastic and chance-constrained programming. It enables the IES to determine the optimal bidding electricity quantities in the day-ahead market in a multi-energy complementary manner by incorporating the potential real-time clearing price scenarios, with the uncertainties introduced by RDER generation and multi-energy loads efficiently addressed using chance constraints. Herein, a gradient boosted regression tree (GBRT) based quantile forecasting method is developed to equivalently reformulate the chance constraints to tractable deterministic constraints without any prior knowledge or probability distribution assumptions. The proposed day-ahead bidding strategy is extensively assessed through simulation experiments in the PJM and Guangdong electricity markets and the numerical results confirm the effectiveness of the proposed solution.

Low-carbon economic dispatch of regional integrated energy system based on carbon-oxygen cycle

In order to solve the contradiction between energy supply and carbon emissions, using P2G to combine oxy-fuel combustion power plant with gas turbines. According to the laws of mass conservation and energy conservation, carbon and oxygen elements are introduced into the model in the form of gases to construct an energy hub model for the carbon oxygen cycle system. To solve the problem of time imbalance between interconnected devices, a flexible operation mode using storage devices as connection hubs is proposed. Based on the constraints of the system’s operating mechanism, study the variation range of the operating range and establish an objective function with the optimal operating cost. Optimize the operation of the combined system, study the change interval of its operation range, and analyze the scheduling model under three different operation models. Low-Carbon economic model of combined system is established in regional integrated energy system. The results show that the basic carbon oxygen cycle model can save 9.14% economic cost and reduce 44.05% carbon emission. The carbon oxygen cycle capacity increasing mode can save 13.05% economic cost and reduce 59.91% carbon emission. Carbon and oxygen circulation system in improving renewable energy consumption and reducing carbon dioxide emissions is verified by an example.

Optimal configuration and operation of the regional integrated energy system considering carbon emission and integrated demand response

Under the “carbon peaking and carbon neutrality” development strategy, in order to suppress load fluctuations and promote renewable energy consumptions in the regional integrated energy system involving concentrating solar power stations, a double-layer optimization model based on the improved non-dominated sorting genetic algorithm-II (NSGA-II) and mixed integer linear programming (MILP) is proposed. The upper layer completes the capacity configuration process based on multiple objectives to minimize the annual planning cost and the net emission of pollutants. The lower layer is designed to minimize the annual operating cost and optimize the output of the devices and the load curves through the participation of the integrated demand response process for flexible loads and the whole process of carbon emission including carbon capture, carbon utilization, and carbon trading mechanisms to obtain the best operating plan. The final results indicate that the participation of concentrating solar power stations can improve the level of coordinated optimization, and the improved NSGA-II is stronger than the conventional one in convergence ability. Besides, considering the whole process of carbon emission and integrated demand response is capable of decreasing the annual operating cost and net carbon emission to improve the economy and environmental protection of the system significantly.

Strategies for improving resilience of regional integrated energy systems in the prevention–resistance phase of integration

The construction of integrated energy systems can help improve energy efficiency and promote global energy transition. However, in recent years, the occurrence of extreme natural disasters has brought certain threats to the safe and stable operation of the integrated energy system. Thus, it is necessary to improve the ability of the integrated energy system to resist disasters, reduce disaster losses, and restore energy supply as soon as possible, i.e., improve its resilience. Considering the influence of pre-disaster prevention measures and disaster-time operational measures on system disaster resilience and the correlation between the two, this paper proposes a system hardening strategy based on three-layer robust optimization. The upper layer formulates the optimal hardening strategy of the system before the disaster event occurs, the middle layer identifies the failed elements in the worst disaster situation, while the lower layer realizes the system operational optimization by coordinating the energy storage charging and discharging plan of each subsystem. The strategy can reduce the total supply shortage of the integrated energy system and improve the flexibility of the system in the pre-disaster prevention and disaster resistance integration stages.

Negative carbon optimal scheduling of integrated energy system using a non-dominant sorting genetic algorithm

An integrated energy system, as a system where multiple energy sources coexist, is important for achieving energy conservation and emission reduction and upgrading the energy structure. In this study, a regional integrated energy system capable of absorbing CO2 emissions is proposed. The system can absorb 38.24% of the CO2 generated in the system by using the alkaline solution produced by electrolysis of water for hydrogen production. At the same time, the system can recover the waste heat generated by electrolysis of water and fuel cells, which can meet 42.42% of the heat demand. In order to better achieve efficient utilization between energy sources of the integrated energy system, a nonlinear mixed integer dynamic scheduling optimization model is constructed with multiple objectives of minimizing economic operating costs, CO2 emissions, and energy consumption. The model is solved using a non-dominated ranking genetic algorithm, and the optimal scheduling scheme is determined by combining compromise schemes. Meanwhile, the system can spontaneously select the most appropriate scheduling strategy based on the proposed scheduling model. The results show that it saves 7.23% of operation cost, 33.04% of CO2 emission, and 9.94% of energy consumption compared with the conventional cooling, heating, and power triple-supply system. The system is not only a stable source of energy such as electricity and hydrogen, but also achieves energy saving and emission reduction.

Bilevel Optimal Scheduling of Electric Bus Fleets in Regional Integrated Electricity–Gas–Heat Energy Systems

Proliferation of electric buses (EBs) is widely acknowledged as a viable solution to reduce carbon emissions while addressing the demands of ground transportation. This paper investigates how to optimally schedule EBs in regions where heterogeneous energy resources, such as electricity, gas, and heat, are integrated as a regional integrated energy system (RIES). A novel bi-level optimal scheduling framework considering EBs’ operation schedules and vehicle-to-grid (V2G) responses is proposed in this work. Firstly, instead of charging-by-need (e.g., low SOC), this paper considers charging-by-scheduling (e.g., low electricity price) to optimize regional energy cost by explicitly incorporating coupled constraints from EB operation schedules, renewable energy generations, gas network models, and heat supply demands. The upper level scheduling strategy is formed to minimize the operation cost of the entire RIES. Secondly, EB operation strategies and energy generation & heat conversion devices (e.g., CHP, electric boilers, and gas boilers) are further optimized in the lower level to minimize fluctuation in the total electrical load. Finally, numerical simulation results of coordinated scheduling of EB charging, renewable integration, and gas supply to meet electrical and heat demands under different scenarios are compared and analyzed. Simulation results based on five scenarios show that the bi-level model and the two-stage optimal scheduling method can realize the optimal operation cost and reduce the electrical load fluctuation of the system.

Research on Optimal Operation of Regional Integrated Energy Systems in View of Demand Response and Improved Carbon Trading

In order to solve the difficulties of dispatching the regional integrated energy system (RIES) under the operating conditions of multi-energy complementary mechanisms, as well as to achieve the purpose of economic operation and low carbon operation of the system, an optimal dispatching model of RIES, including demand response (DR) and an improved carbon trading mechanism (ICTM), is proposed. Firstly, a demand response model is established, the cooling, thermal, electricity, and gas load models under demand response are built, and then an improved customer satisfaction model is proposed based on the four demand response load models. In addition, since EV trips fit a normal distribution, the charging load of EVs is predicted using a Monte Carlo method and incorporated into RIES as a demand-side load; moreover, for EVs, an improved genetic algorithm is used to optimize EV charging, aiming to reduce the peak-to-valley difference; secondly, carbon emission quotas are provided for systems and EVs based on the baseline method and gratuitous allocation, and a carbon trading model is constructed based on carbon quotas and actual A carbon trading model for the system and EV is constructed based on the carbon allowances and actual carbon emissions; finally, four operation scenarios are set up in this paper, and the unit output scheme is developed with the objective of achieving the lowest total system operation cost and lowest carbon emissions. The four typical scenarios are solved using the MATLAB/CPLEX solver and compared for analysis. The simulation results show that an improved genetic algorithm for optimizing the ordered charging method of electric vehicle charging reduces the peak valley difference by 23.06%, and the total operation cost and carbon transaction cost are reduced by 16.13% and 83.10%, respectively, which can provide a reference for the environmental protection and economic dispatch of RIES.

Optimal configuration for regional integrated energy systems with multi-element hybrid energy storage

This paper proposes a configuration method for a multi-element hybrid energy storage system (MHESS) to address renewable energy fluctuations and user demand in regional integrated energy systems (RIES). To reduce the investment cost of energy storage applications in RIES, a multi-timescale capacity configuration model is formulated, containing a day-ahead power planning model to optimize the power output of energy supply equipment on the hour-level scale and a day-in power operation model that considers the power response characteristics of MHESS to smooth out renewable energy fluctuations on the minute-level scale. In addition, an active energy storage operation strategy is proposed to minimize the configuration investment of MHESS in the day-ahead planning stage. The empirical mode decomposition algorithm is employed to decompose the target power of MHESS to derive the optimal capacity configuration and power output of each energy storage unit. The simulation results indicate that the proposed method is successful in reducing the investment cost when compared to both a passive and a simple Li-ion battery method. Specifically, the proposed method reduced the investment cost by 8.8% and 183.64%, respectively. This work provides a valuable reference for RIES optimization and further exploration of the potential for MHESS development.

Multi-stage distributionally robust optimization for hybrid energy storage in regional integrated energy system considering robustness and nonanticipativity

Hybrid energy storage system (HESS) has advantages in coping with the uncertainty of renewable energy and improving the stability of regional integrated energy systems (RIES). Few of the current HESS uncertainty scheduling methods consider both robustness and nonanticipativity, which may result in scheduling strategies that are not feasible in practice. Therefore, this research constructs a HESS (containing electric, thermal, hydrogen and natural gas storage devices) and forms a hybrid energy storage operator (IHESO) with an independent market position based on the HESS to provide energy services. Subsequently, a novel multi-stage distributionally robust optimization (MSDRO) method is established to maximize HESS revenue. Robustness and nonanticipativity can be satisfied in the uncertainty scheduling process of this method. After that, the stochastic dual dynamic integer programming (SDDiP) algorithm is introduced to solve multi-stage nested problems of complex systems. Finally, simulations are conducted in the actual system of three seasons and five scenarios. The conclusions are as follows: 1) The scheduling strategy provided by the MSDRO model and SDDiP algorithm can ensure effective operation based on satisfying robustness and nonanticipativity. 2) Compared with MSO, TSDRO and MSRO, the method proposed can improve the HESS economy and stability, increase renewable energy consumption and reduce carbon emissions.

Study on the Optimal Configuration of GSHP in Regional Integrated Energy Systems Considering the Uncertainty of New Energy

The study of regionally integrated energy system optimization problems is of great significance and usefulness for the development of the energy Internet. Regional integrated energy system planning and optimization modeling is the basis for rational system configuration and economical operation. It is one of the key issues in the development of integrated regional energy systems. The optimization of a regionally integrated energy system is studied in this paper. The uncertainty of wind power generation and photovoltaic power generation and the characteristic of CHP is considered. Were established. On this basis, Considering the uncertainty of new energy, a GSHP optimal allocation model based on two-layer optimization is established. A two-level optimization model is introduced. A genetic algorithm is applied to the upper layer, and a genetic algorithm is applied to the lower layer to analyze the nonlinear programming problem. Finally, different system configuration schemes are proposed and computationally solved, combined with examples for case studies to verify the feasibility of the established regional integrated energy system optimization model.

Risk assessment for cascading failures in regional integrated energy system considering the pipeline dynamics

With the close coupling of multiple energies (i.e., electricity, gas, cold, and heat) in a regional integrated energy system (RIES), failures in an energy subsystem can easily propagate to other energy subsystems via multi-energy coupling devices and result in cascading failures. Considering the dynamics of pipeline energy transmission and pipeline leakage failures, a novel dynamic model of analyzing the cooling/heating or gas pipeline leakage failure is proposed. Then a risk assessment model for cascading failures in a RIES is established, including the calculation of the probability and severity of cascading failure chain (CFC). A model for determining the failure probability of multi-energy coupling devices based on the device operation state is established, and a method for calculating the probability of CFC based on Bayes theorem is proposed. Considering the uncertainty and time correlation of renewable power, an ambiguity set based on the Jensen–Shannon divergence and the moment information of actual historical data is proposed, and then a distributionally robust optimization model for calculating the severity of CFC is established and solved by the column-and-constraint generation algorithm. Test results on a RIES demonstrate the high computational accuracy and efficiency of the proposed method.

Collaborative optimization method of the regional integrated energy system for energy cascade utilization

Energy is an important resource, and its main reserves are obviously insufficient compared with consumption. The energy structure is mainly dominated by fossil energy. The pollution emissions from energy utilization are becoming more and more serious. The energy problem has seriously affected the development of new urbanization. This paper studies the optimal scheduling problem of typical urban integrated energy systems participating in different demand response methods. Based on the traditional power demand response model, the demand response model is constructed according to the equivalent thermal parameters. Its model can optimize the electricity and gas load curves and realize the balance of electricity, heat, and gas in an economical and environmentally friendly way. It can also reduce the total cost of the economic operation of the system and reduce the overall carbon emissions of the system. Through the optimization and regulation of multi-type “source-storage-load”, multi-time scale, and multi-objective joint for the distributed thermal power, the matching of thermal and electric hybrid energy and the peak and valley of the power grid can be realized.

Cluster partition for distributed energy resources in Regional Integrated Energy System

Fully utilizing local clean energy by regional Integrated Energy System is a effective way to alleviate energy crisis and reduce carbon emissions. Due to clean energy resources e.g., photovoltaic, wind power and low-grade heat distribute in region which results to be difficult to integrate, this paper provide improved two-steps k-means arithmetic for cluster partition of Regional Integrated Energy System for utilizing distributed energy. Firstly, the weight index is defined for direction of cluster partition. Secondly, according to the resource endowment and located position of distributed energy, the initial energy cluster partition and cluster center considering weight coefficient is determined. Finally, load is divided into the cluster according to the distance of source load and matching coefficient. The practical case indicate that the method proposed can effectively realize cluster partition and integration of distributed energy.

Multi-parameter cooperative optimization and solution method for regional integrated energy system

As an energy system that uses the power grid and heat network to coordinate the energy interaction between subsystems in the region, the regional integrated energy system can further reduce the system's operating cost and improve the energy utilization rate. However, in the optimization of regional integrated energy systems, there is difficulty in balancing the accuracy of the results and the calculation time. Therefore, a nonlinear collaborative optimization model of a regional integrated energy system is established in this paper, which fully considers the electricity and heat sharing between energy stations. Subsequently, a three-layer-nested co-optimization method for a regional integrated energy system is proposed. The outer layer, middle layer, and inner layer optimize the rated capacity of the internal combustion engine, electric refrigeration ratio, and partial load rate of the internal combustion engine, respectively. Finally, the three-layer-nested co-optimization method is verified from the total system cost, energy utilization rate, and calculation time. The results show that compared with the exhaustive search method and genetic algorithm, the cost per unit area of the system is about 102.99 ¥/m2 (reduced by 1.00 ¥/m2), the energy utilization rate is 70.47% (increased by 2.43%), and the calculation time is about 50–100 h. The three-layer-nested co-optimization method significantly shortens the model's solution time while ensuring the optimization results' accuracy. Finally, this paper can provide a research idea for optimizing regional integrated energy system design.

Integrated demand-side management for multi-energy system based on non-cooperative game and multi-energy pricing

This paper proposes an integrated demand-side management (IDSM) approach based on non-cooperative game and multi-energy pricing strategy for a regional integrated energy system (RIES). On the demand side, the competition among multi-energy users was modeled as a non-cooperative energy consumption game, and the conditions to ensure the existence and uniqueness of the Nash equilibrium of the non-cooperative game were given. Considering the privacy of user information, a distributed multi energy consumption control algorithm based on dual theory and gradient descent method was designed to search for the optimal multi-energy consumption and the multi-energy daily retail price. In addition, for the energy supply side, the scheduling strategy of energy hub (EH) was proposed based on linear programming (LP) theory to optimize equipment operation. The results show that this approach can enable users and EH to coordinate optimization, so as to reduce the peak-to-average ratio (PAR) and energy cost of the system, and ensure the balance between supply and demand of multi-energy.

Synergetic planning method for energy stations, pipeline networks, and demand response

Rational planning for energy stations and their lower-level energy supply pipeline networks is vital for improving the economy of the regional integrated energy system. Many studies have been focused on synergetic planning for energy stations and pipeline networks, but few have been oriented from the perspective of synergetic planning for energy stations, pipeline networks, and demand response, which may result in a redundant configuration of the regional integrated energy system. This paper proposes a synergetic planning method for energy stations, pipeline networks, and demand response. Initially, the impact of demand response on the traditional synergetic planning for energy stations and pipeline networks is analyzed. Subsequently, a synergetic planning method for energy stations, pipeline networks, and demand response is proposed to determine the optimal locations of energy stations, the optimal equipment capacity of energy stations, the optimal demand response configuration, and the optimal layout of pipeline networks. Finally, case studies are conducted to verify the effectiveness of the proposed method. Compared with the traditional synergetic planning method for energy stations and pipeline networks without considering demand response, the proposed method can reduce the construction cost of energy stations by approximately 4.8% and pipeline networks by around 8.5%. Thus, the proposed method can be applied for planning energy stations and pipeline networks.

Co-optimization method research and comprehensive benefits analysis of regional integrated energy system

The nearly-zero energy community composed of contiguously nearly-zero energy buildings can effectively reduce the operational energy consumption of buildings by adopting appropriate active and passive technologies, which is the main development direction of future buildings. However, there are few studies on the collaborative optimization of nearly-zero energy community supply systems. Therefore, a nonlinear cooperative optimization model of the regional integrated energy system with multi-region energy sharing and multi-energy storage is constructed in this paper. A two-layer collaborative optimization method is proposed, which optimizes the upper layer's renewable energy and energy storage capacity and the operation dispatching in the underlayer. Finally, the overall benefit, typical daily energy scheduling, and the energy sharing and storage impact on renewable energy utilization of the system when it supplies energy to a nearly-zero energy community are studied. The research results show that compared with the isolated integrated energy system, the supply cost, primary energy consumption, carbon emission and interactive power per unit area of the regional integrated energy system are reduced by 3.45 CNY/m2, 3.95 kWh/m2, 1.35 kg/m2 and 1.66 kWh/m2, respectively. In addition, multi-region energy sharing and multi-energy storage can effectively improve the self-consumption/sufficiency ratio of photovoltaic and wind power generation and solar thermal collector heat collection. This study provides a feasible solution for applying nearly-zero energy communities in regional integrated energy systems.