Park Integrated Energy System Research Articles

Optimal scheduling of integrated wind- photovoltaic-hydrogen energy system considering hydrogen application and waste heat recovery

In order to further promote the use of renewable energy sources such as wind-pv-hydrogen and carbon emission reduction in the park integrated energy system, battery fuel cell waste heat recovery is less considered and has a greater economic value, in this context, the paper proposes an optimal scheduling strategy for the park-level integrated energy system with hydrogen fuel cell vehicles (HFCV) taking into account the fuel cell waste heat recovery in order to improve the economic efficiency and stability characteristics of the system. In this paper, a fuel cell waste heat recovery model is constructed, followed by the introduction of a tiered carbon trading model, and then a scheduling model is constructed with the objective of minimizing the sum of the system's energy purchase cost, carbon trading cost, and the penalty cost for abandoning the wind and solar, and solved by CPLEX. Finally, the effectiveness of the proposed strategy is demonstrated through the analysis of a simulation example, which leads to a 30.7% reduction in the total system cost, a reduction in carbon emissions by 18.8% and a 34.2% reduction in the system's purchased energy cost compared to the conventional model.

Coordinated reactive power planning in an industrial park integrated energy system using support vector regression

In this study, we develop and present a comprehensive multi-objective optimization model for reactive power planning in industrial park integrated energy systems (IPIES), addressing the challenges posed by extensive inductive loads. Our model prioritizes enhancing system resilience through three key objectives: minimizing total costs, improving static voltage stability margins, and enhancing voltage recovery capabilities. We employ support vector regression (SVR) to estimate voltage recovery metrics effectively, using reactive power capacity as input. The optimization process leverages the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) for efficient solution finding. Validation on the IEEE-39 bus system demonstrates the model’s effectiveness, with SVR significantly reducing computational demands while maintaining satisfactory accuracy.

Edge–Cloud Collaborative Optimization Scheduling of an Industrial Park Integrated Energy System

Due to the large proportion of China’s energy consumption used by industry, in response to the national strategic goal of “carbon peak and carbon neutrality” put forward by the Chinese government, it is urgent to improve energy efficiency in the industrial field. This paper focuses on the optimization of an integrated energy system with supply–demand coordination in an industrial park. This optimization is formulated as a “node-flow” model. Within the model, each node is designed according to the objective function of its own operation and the energy coupling relationship. The flow model is designed based on the energy flow interaction relationship between each node. Based on the “node-flow” model, an edge–cloud information interaction mechanism based on energy transfer balance between nodes is proposed to describe the way the system interacts with information, and a distributed iterative optimization algorithm based on edge–cloud collaboration is designed to realize the optimization decision of each node. The performance of the method proposed in this paper is demonstrated using a practical case study of an industrial park integrated energy system in Xinjiang. The results show that the proposed model can effectively improve the utilization efficiency of multi-energy synergy and complementation in the industrial park, and the proposed algorithm can shorten the solution time by more than 50% without significantly affecting the accuracy of the solution.

Low carbon economic scheduling model for a park integrated energy system considering integrated demand response, ladder-type carbon trading and fine utilization of hydrogen

With the intensification of energy crisis and the aggravation of greenhouse effect, It is particularly essential to develop a sustainable energy system. For this reason, a low-carbon economic dispatch model for a park integrated energy system considering integrated demand response and ladder-type carbon trading is designed. First, a refined model of hydrogen utilization is developed, and hydrogen-enriched natural gas as hybrid energy to extend the scenario of hydrogen utilization. Second, in order to investigate the effect of demand response mechanism and carbon trading mechanism on the system, an integrated demand response model for electricity-heat-gas load and a ladder-type carbon trading model that takes into account the carbon emissions of the whole process are constructed respectively. Finally, based on above models, a low-carbon economic scheduling model is established to optimize the total cost of the system. The simulation results show that the refined modeling and hydrogen-doped operation of the equipment can help reduce the system's carbon emissions. When the integrated demand response mechanism and the ladder-type carbon trading mechanism are considered together, the carbon emissions of the system are greatly reduced, while its total cost can be kept low.

Low carbon dispatch of the park integrated energy system based on the electric vehicles flexible load storage characteristics

The integrated energy system is an efficient way of utilizing energy in industry park. However, with the massive integration of renewable energy and disorganized charging of electric vehicles, the safe operation of this system faces several challenges. To address these issues, we propose a novel dispatch model that incorporates the flexible load characteristics of electric vehicles clusters. Firstly, we elucidate the operational framework for the integrated energy system in parks and establish models for users and microgrid operators incorporating carbon trading mechanisms. These models can effectively portray how an integrated energy system operates within a park setting. Secondly, using charging data from parks, we uncover potential dispatchable charging/discharging capacities for electric vehicles clusters and formulate strategies to utilize electric vehicles as flexible loads in our dispatch operation policy. By appropriately regulating electric vehicles charging/discharging behaviors, demand-supply balance within the system can be better achieved. Subsequently, aiming to maximize benefits for all entities in the park area, we construct a master-slave game model that involves multiple users and microgrid operators. Lastly, employing reinforcement learning concepts, we establish an equivalent power output models for wind turbines, photovoltaic power generation and apply it to an integrated energy system in an industrial park in a specific city. An analysis reveals that our proposed model not only minimizes cost associated with energy storage equipment but also significantly reduces carbon emissions; yielding mutual benefits for both microgrid operators and users.

Robust Bilevel Optimal Dispatch of Park Integrated Energy System Considering Renewable Energy Uncertainty

This paper focuses on optimizing the park integrated energy system (PIES) operation, and a robust bilevel optimal dispatch is proposed. Firstly, the robust uncertainty set is constructed based on the K-means++ algorithm to solve the uncertainty of renewable energy sources output in PIES. Then, the bi-level dispatch model is proposed, with the operator as the leader and consumers as the follower. The upper model establishes an electricity-heat-gas integrated energy network, and the lower model considers the demand response of consumers. Optimizing the pricing strategies of energy sources to determine the output of each energy conversion equipment and the demand response plan. Moreover, analyzing the decision-making process of the robust bi-level model and the solution method is given. Finally, case studies show that the proposed dispatch model can increase operator profits and reduce consumers’ energy costs. The in-sample and out-of-sample simulations demonstrate that the proposed ellipsoid uncertainty set possesses high compactness, good robustness, and low conservatism.

A model-free optimal operation strategy of diversified demands-park integrated energy system considering energy cascade utilization

In order to improve the energy efficiency of park integrated energy system, this work proposes a model-free optimal operation strategy of diversified demands-park integrated energy system considering energy cascade utilization. First, by considering diversified demands of various users for energy, an energy cascade utilization mode and its corresponding multi-dimensional energy supply and demand balance equation is developed. Moreover, in view of the above structure, a model-free DD-PIES optimal operation strategy is further proposed. Among them, the model-free approach is realized on the basis of the interaction between agent and environment, combined with sufficient exploration and consequences learning of its behavior through experience. Compared to the previous works, this proposed strategy has good self-learning ability for multiple uncertainties of renewable energy and energy demand, and cuts down on inefficient exploration during the training process, while guarantee the security during the execution of actions by the agent. Finally, the rationality and validity of the proposed strategy in reducing operating cost and improving energy efficiency of the system are verified by a case study.

Integrated energy system scheduling considering the correlation of uncertainties

Under the circumstances of “double carbon”, it is imminent to promote the transition of energy. Integrated energy systems (IES) are one of the critical technologies for the energy transition. To achieve optimal operation of IES with uncertainty to reduce system operational cost and carbon emission. In this paper, we propose an optimal scheduling method considering the correlation between uncertainties with source-source, load-load, and source-load. Scenario generation with Wasserstein deep convolutional generative adversarial network-gradient penalty (WDCGAN-GP) captures the correlation between uncertainties. The laddered carbon transaction mechanism is introduced to control carbon emissions. The simulation analysis of the method is performed with the case of the park integrated energy system (PIES) to explore the impact of correlation on IES scheduling. The simulation results show that the carbon emission of laddered carbon transactions is reduced by 8.75% compared with the conventional, while the total cost is increased by 11.09%; on the basis, after considering the correlation, the carbon emission is reduced by 6.22%, and the total cost is decreased by 5.62%. Clearly, the correlation provides financial and environmental benefits to the system. This study provides theory instruction for IES scheduling under uncertainty, which with enormous potential for engineering applications.

Optimal Configuration of Power/Thermal Energy Storage for a Park-Integrated Energy System Considering Flexible Load

The park-integrated energy system can achieve the optimal allocation, dispatch, and management of energy by integrating various energy resources and intelligent control and monitoring. Flexible load participation in scheduling can reduce peak and valley load, optimize load curves, further improve energy utilization efficiency, and reduce system costs. Based on this, firstly, a flexible power-load model is established considering the translatable load, transferable load, and reducible load; and a thermal flexible load model is established based on the fuzziness of user perception of temperature in this study; then, the mixed integer linear programming method is adopted, and the sum of the carbon transaction cost, operation and maintenance cost, compensation cost, power purchase cost, gas purchase cost, wind and light abandonment penalty cost and investment cost of the system is minimized as the objective function, and the configuration of the integrated energy system is optimized, and the optimal capacity of each equipment and the output of each period are obtained. Finally, taking an industrial park in Liaoning Province of China as an example, the analysis is carried out. The example results show that by scheduling the flexible electrical load and flexibly adjusting the indoor temperature, renewable energy consumption can be promoted, and electricity load and heat-load curves can be optimized to increase the installed capacity of wind turbines, reduce the capacity of gas turbines, batteries, and heat-storage tanks, improve system economy, and improve the penetration rate of renewable energy.

Robust stochastic low-carbon optimal dispatch of park-integrated energy system with multiple uncertainties from source and load

To realize the cascaded utilization of energy, improve the effective utilization of energy, and further reduce the carbon emissions of integrated energy systems a robust stochastic low-carbon optimal dispatch model with economy, environmental protection and reliability is developed for a park-integrated energy system wherein the multiple uncertainties brought by source and load are fully considered. First, a two-stage robust optimization algorithm is employed to handle uncertain wind power generation. A multi-case analysis method for the uncertainties of photovoltaics and load is proposed based on an improved centralized reduction algorithm. Then, considering the depreciation of the weighted average of the comprehensive operation cost, carbon emissions, and energy undersupply rate, a robust stochastic optimal dispatch model can be derived and efficiently solved by using a multi-objective fuzzy optimization algorithm with an improved membership function. Finally, by comparing the four cases, the simulation results show that the computational complexity and calculation time of the system can be reduced, the trimming result errors can be decreased, and a balance between economy, environmental protection, reliability, and robustness can be achieved.

BAS-PSO Algorithm for Integrated Energy System Optimization of Multiple Energy Supply Facilities

Regional low carbon will play an important role in the path of achieving the “double carbon” goal, and there are still many key technologies to be broken in its planning research. In this paper, an optimal scheduling model of integrated energy system with multiple energy supply devices is established by using Beetle Antennae Search-Particle Swarm Optimization (BAS-PSO) algorithm. First, in the scheduling model, a carbon trading mechanism is introduced and a stepped carbon trading cost model is constructed to constrain the carbon emissions of the plant. Then, using drosophila algorithm, the premise of whether wind power generation and photovoltaic power generation need to be built is determined by judging the economics of wind power generation and photovoltaic power generation in the construction area, and then the target power consumption curve and new energy power supply output curve are fitted. Then use the life cycle analysis method to analyze the carbon emissions generated by electric energy storage equipment, consider the carbon trading mechanism in the system economic operation model, and solve the model by BAS-PSO algorithm to overcome the problems of local optimum and slow convergence speed. Finally, a typical park integrated energy system is simulated to analyze the economic operation conditions and energy efficiency level of the system before and after participation in demand response. The innovation of this paper lies in considering the carbon trading mechanism and solving with the optimized BAS-PSO algorithm. Considering carbon trading can effectively improve the system wind power consumption capacity, and the optimized BAS-PSO algorithm improves the defects of traditional PSO. The simulation analysis results show that the established optimal scheduling model with multiple energy supply devices can realize the optimal operation of the integrated energy system of the park in the demand response environment; the improved BAS-PSO finds the lowest energy consumption within 100 iterations in 21 out of 50 iterations; the improved BAS-PSO algorithm reduces the energy consumption by 27.1 kW on average.

Research and application of load forecasting method for park integrated energy system based on XGBoost algorithm

Load forecasting is the basis of energy management and optimal dispatching of the park-integrated energy system, and its forecasting accuracy directly affects the overall operation performance of the system. Therefore, this paper proposes a load forecasting method for the park-integrated energy system based on the XGBoost algorithm. This paper first establishes the simulation analysis model of photovoltaic power generation and electric vehicle charging and discharging in the park and then carries out the supply and demand forecast and load characteristic modeling of the park’s energy system. Finally, this paper constructs the power prediction optimization control flow based on the XGBoost prediction model and uses the mean absolute percentage error (MAPE) and root mean square error (RMSE) as two evaluation indicators. The validity of the proposed method is verified by the data of the actual micro energy system in the park.

Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system

With the increasing uncertainty of energy supply side output, fully encouraging users to participate in demand response through different types of demand response incentive mechanisms has become one of the effective ways to deal with the uncertainty of integrated energy system operation and improve the overall energy efficiency. However, in existing studies, the coordination of uncertainty handling, optimization of demand response incentive strategies, and demand response measures at different time scales have not been adequately considered in the operation of integrated energy systems. Based on these considerations, this paper proposes a multi time-scale game optimization scheduling model for Park-level Integrated Energy System considering multiple types of demand response models. In the day-ahead stage, a Park-level Integrated Energy System optimization game scheduling model based on the demand response comprehensive incentive mechanism is established, and the uncertainty of the predicted value of distributed renewable energy and multi-type energy load was characterized based on the fuzzy chance-constrained programming method. In the intraday and real-time stages, a rolling optimization scheduling model is established with the minimum cost of Park-level Integrated Energy System operator scheduling. For the proposed model, an improved particle swarm optimization algorithm and an iterative solution strategy of CPLEX solver are introduced. Finally, the simulation results of an actual case show that the proposed model can effectively improve the Park-level Integrated Energy System operator and user economy while ensuring reliability.

Study on Integrated Demand Response Strategy and System Coordination and Optimization of Park Integrated Energy System

The need for flexibility in integrated energy systems is progressively becoming more demanding as these systems expand. Three types of demand response are introduced. An integrated demand response strategy is developed to address the situation based on an analysis based on the fundamental model of the Park Integrated Energy System. Additionally, the coordinated optimization model of the Park Integrated Energy System is created to minimize operational costs while considering a variety of restrictions.

Quantitative study on steam system thermal inertia based on a dynamic hydraulic analytical model

Integrated energy system (IES) in industrial parks is pivotal to China's carbon neutrality path, where steam plays a critical role as an energy carrier and raw materials in the production process. Utilization of the thermal inertia of steam systems may become a means to address challenges in industrial park IES dispatch. However, researchers have less discussed the dynamic modeling and inertia quantification of industrial steam systems compared to district heating. This work proposes a dynamic hydraulic analytical model (DHAM) of steam systems reflecting the spatial and temporal transportation of steam by introducing the reference temperature and momentum linearization assumptions. Further, a steam thermal inertia model (STIM) which comprises five storage key parameters and two indexes, is developed to quantify the thermal inertia based on DHAM. Validation results of DHAM in an industrial park demonstrate that the relative error is less than 3%, achieving high accuracy. Whilst the thermal inertia of a 163-node steam system in two operation conditions is quantified and compared. Results show that the net heat storage of the steam system could reach 7440.30 kg while the positive utilization rate in both scenarios never rises above 3%. It indicates that the thermal inertia as virtual storage can fully cover the dispatching demand while is not utilized and lacks the regulation strategy of active charging in operation. DHAM and STIM proposed in this study lay the quantitative basis for steam system analysis and operation optimization of industrial park IES.

Multi-energy collaborative optimization of park integrated energy system considering carbon emission and demand response

Park integrated energy system (PIES) has become a key link of efficient energy conservation and carbon emission reduction. This paper proposes a multi-energy collaborative optimization method of PIES considering carbon emission and demand response (DR). Firstly, the typical structure of the electricity-thermal-gas cogeneration PIES including combined heat and power (CHP), heat pump (HP) and energy storage (ES) is built. Secondly, a ladder carbon trading model for PIES considering carbon quota and actual carbon emission is established. On this basis, a multi-objective collaborative optimization model considering the operation cost, energy utilization efficiency and consumption rate of renewable energy is established, and the multi-objective problem is solved by a multi-objective particle swarm optimization algorithm (MOPSO). Then, taking a typical PIES as an example, the operation conditions of the system before and after DR are analyzed, and the results show that the established model can realize the economic and low-carbon operation and improve renewable energy consumption rate. The numerical results show that when participating in DR, the operation cost of PIES is reduced by 10.18% and the carbon emission is reduced by 3.41%. Finally, the impact of carbon trading price on the operation cost, energy utilization efficiency and consumption rate of renewable energy is analyzed.

Optimal planning for industrial park-integrated energy system with hydrogen energy industry chain

Establishing an industrial park-integrated energy system (IN-IES) is an effective way to reduce carbon emission, reduce energy supply cost and improve system flexibility. However, the modeling of hydrogen storage in traditional IN-IES is relatively rough. In order to solve this problem, an IN-IES with hydrogen energy industry chain (HEIC) is proposed in this paper. Hydrogen production, transportation, and storage technologies are applied in HEIC. Firstly, a novel long-term hydrogen storage model considering different time steps is presented. Secondly, hydrogen compressor models considering different pressure ratios are further employed. On this basis, the impact of the HEIC on the planning and operation results of IN-IES is studied. Finally, the superiority and the effectiveness of the proposed model and planning method are verified by simulation cases.

Two stage robust planning of park integrated energy system considering low carbon

The development of integrated energy systems is a significant way of improving energy efficiency and encouraging renewable energy absorption. However, the uncertainty of renewable energy and loads raise severe challenges to integrated energy system planning. Besides, under the low carbon background, the relationship between reducing carbon emissions and increasing the absorption of renewable energy is not investigated clearly in the planning of the park integrated energy system. Thus, this paper proposes a two-stage robust planning method considering low carbon and uncertainty. Firstly, the input-output model of energy conversion equipment in the park integrated energy system is described. Secondly, the objective function and constraints and are set up. Specifically, the carbon emission limits and renewable energy penetration target are introduced in the constraints. On this basis, the two-stage robust planning model with min-max-min structure is established, after transformation, column-and-constraint generation is used for the solution. Case study shows the ability and cost of the park integrated energy system to promote low-carbon emission reduction and renewable energy consumption. Moreover, it proves that although there is some synergy between the low carbon target and the renewable energy consumption target, maximum renewable energy consumption does not mean minimum carbon emissions, and vice versa, and in some cases increasing renewable energy penetration does not necessarily reduce carbon emissions.

Optimal Dispatch of Regional Integrated Energy System Group including Power to Gas Based on Energy Hub

Different renewable energy resources and energy demands between parks lead to waste of resources and frequent interactions between the regional distribution grid and the larger grid. Hence, an optimal dispatching scheme of the regional integrated energy system group (RIESG), which combines the power-to-gas (P2G) and inter-park electric energy mutual aid, is proposed in this paper to solve this problem. Firstly, for the park integrated energy system (PIES) with various structures, the coupling matrix is used to describe the input-output relationship and coupling form of multiple energy sources in the energy-hub (EH), which linearizes the complex multi-energy coupled system and is more conducive to the solution of the model. Secondly, the electrical coupling relationship of the system is improved by adding P2G to enhance the system’s ability to consume renewable energy. Moreover, the installation cost of P2G is introduced to comprehensively consider the impact of the economic efficiency on the system. Finally, to minimize the network loss of energy flow, the optimal dispatching model of RIESG with P2G conversion is constructed through the electric energy mutual aid among the parks. The simulation shows that compared with the independent operation of each park’s integrated energy system (IES), the proposed optimal dispatching strategy of RIESG achieves the mutual benefit of electric energy among park groups, reduces the dependency on the large power grid, and effectively improves the economy of system groups. In this condition, the renewable energy consumption rate reaches 99.59%, the utilization rate of P2G increases to 94.28%, and the total system cost is reduced by 34.83%.

Distributed Cluster Regulation Strategy of Multipark Integrated Energy System Using Multilayer Deep Q Learning.

The power system is evolving from a single energy system to an integrated energy system. In order to further improve the power generation and consumption balance capacity of the park integrated energy system (PIES), the park integrated energy system is gradually transitioning from the single park energy system operation mode to the multipark energy system operation mode. The design of multipark integrated energy system (MPIES) collaborative control strategy will become an important part to improve the power generation and consumption balance ability of the integrated energy system. In order to fully tap the regulation capacity of each PIES, we propose a coordinated control strategy for the integrated energy system in multiple parks considering the flexible substitution interval of multiple types of energy. Firstly, we analyze the influence of the types of regulation resources and the regulation incentive mechanism of the PIES on the regulation flexible range of the PIES. Then, based on the Markov decision process, a distributed cluster regulation model of MPIES considering regulation demand and regulation flexible interval is established. Finally, using multilayer deep Q networks (MLDQN), the distributed cluster regulation optimization algorithm of MPIES is given. The simulation results show that the proposed method can coordinate the regulation ability of each park integrated energy system in the MPIES, give full play to the large-scale advantage of the interconnection of the park integrated energy system, and improve the overall stability of the multipark integrated energy system.