Park Integrated Energy System Research Articles

Low-carbon optimal dispatch of park integrated energy system considering coordinated ammonia production from multiple hydrogen energy

The utilization of ammonia energy has garnered considerable global attention as a viable approach to alleviate pressure on energy supply within park integrated energy systems (PIES) and enhance energy efficiency. However, the introduction of ammonia production requires the system to increase large amounts of hydrogen, diversifying the sources of hydrogen and challenging the coordinated operation of PIES. In this paper, the inertia characteristics of the gas and thermal energy delivery process are utilized to increase the flexibility of the PIES operation. A technological framework for coordinated ammonia production from multi-hydrogen energy in PIES under consideration of having inertia characteristics and a low-carbon operation strategy for ammonia-doped combustion in thermal generation is constructed. Limiting carbon emissions through a stepped carbon trading mechanism in this strategy. In addition, while addressing the ammonia-doped requirement for thermal generation combustion, and by flexibly setting the ammonia doping ratio of thermal generation combustion, the operation of PIES under different ammonia-doped ratios has been analyzed in detail. The results demonstrate that the proposed model enhances the utilization of renewable energy, leading to reduction in total economic costs by 4.52 % and total carbon emissions by 24.81 %. This makes the operation of PIES more economical, low-carbon, and flexible.

Multi‐objective and multi‐stage low‐carbon planning of park integrated energy system considering random outages from superior power grid

AbstractThis article proposes a multi‐objective and multi‐stage low‐carbon planning approach for park integrated energy systems (PIES) considering the impacts of random outages from the superior electrical grid. This approach incorporates optimal multi‐stage construction sequencing and stepped carbon emission trading to leverage the economic and low‐carbon benefits of long‐term planning. First, the islanding modes of PIES are described using four random variables: island type, duration, start time, and typical day of occurrence, from which islanding scenarios are generated based on scenario tree. Next, a multi‐objective planning model that considers both economics and reliability is constructed, with the objectives of minimizing the total lifecycle planning cost and the expected economic loss during islanding. The improved Normalized Normal Constraint (NNC) method is proposed to solve the multi‐objective planning problem. Then, the fuzzy membership function is used to determine the optimal compromise solution, resulting in a planning scheme that balances economic efficiency and supply reliability. Finally, simulations indicate that, at the cost of a slight increase in planning expenses, the proposed model significantly reduces the loss costs under islanding modes compared with single‐objective economic‐focused planning. Additionally, the improved NNC method can achieve a more uniform Pareto frontier compared with the conventional NNC method.

A negative-carbon planning method for agricultural rural industrial park integrated energy system considering biomass energy and modern agricultural facilities

Biomass energy, recognized as a “zero-carbon” energy, has gradually become a crucial approach to promoting the low-carbon transformation of agricultural rural industrial parks (ARIP). Meanwhile, modern agricultural facilities (MAF) can play a significant role in adjusting agricultural load. Therefore, this paper proposes a negative-carbon planning method for the ARIP integrated energy system (IES). Firstly, carbon activities occurring during the operation of ARIP are sorted out and modeled based on the life cycle assessment (LCA) method, especially modeling the “zero-carbon” and “negative-carbon” benefits of biomass energy. At the same time, the models of both agricultural irrigation systems and straw bundling direct combustion heating are constructed in the ARIP planning. Then, a negative-carbon planning model for ARIP IES is established with the goal of minimizing the annualized total cost, where carbon emission reduction is integrated into the optimization objectives through carbon trading. On this basis, a two-stage distributionally robust optimization (DRO) planning model is constructed, where a box-like set and comprehensive norm constraints are used to model the seasonal differences of agricultural industry loads and the uncertainty of probability distribution in photovoltaic (PV) scenarios. Especially, a box set with adjustable uncertainty coefficients is introduced to simulate fluctuations of output in PV scenario. Finally, the CPLEX solver is used to solve the planning model. verify the effectiveness of the proposed planning method in enhancing the economic and environmental effects of ARIP. Results indicate that the proposed planning model can reduce the total cost by 49.584 × 104 $. Besides, the annual carbon emissions can be reduced from 37533.98 t CO2-eq to −15834.5 t CO2-eq. Especially, the carbon sequestration effect of biochar is the key link in achieving “negative carbon” in ARIP.

Low‐carbon optimal scheduling strategy for multi‐agent integrated energy system of the park based on Stackelberg–Nash game

AbstractUnder the background of achieving the dual‐carbon goal, the park is a natural experimental field for practicing the dual‐carbon goal. A low‐carbon optimal scheduling strategy for a multi‐agent park‐integrated energy system (P‐IES) based on the Stackelberg–Nash game is proposed. Firstly, a low‐carbon P‐IES scheduling model is established, considering the two‐stage operation of power‐to‐gas (P2G) technology and reward–punishment stepwise carbon trading mechanism. Then, a two‐layer game optimisation model is proposed with a park‐integrated energy system operator (P‐IESO) as the leader and multiple low‐carbon P‐IESs as followers. Among them, the parks are connected with each other through the power trading channels. Then, the Nash equilibrium solution is obtained by the iterative search method, and the uniqueness of the equilibrium solution of the game is proven, so as to determine the optimal pricing strategy of the operator. Finally, a typical industrial park is taken as an example for simulation verification. The example analysis verifies that the model can effectively reduce the system's carbon emissions and improve the park's economic benefits and low‐carbon levels, thereby achieving the coordinated development of low‐carbon and economic performance in the park.

Optimal scheduling model using the IGDT method for park integrated energy systems considering P2G–CCS and cloud energy storage

To enhance the energy efficiency and financial gains of the park integrated energy system (PIES). This paper constructs a bi-level optimization model of PIES-cloud energy storage (CES) based on source-load uncertainty. Firstly, the scheduling framework of PIES with refined power-to-gas (P2G), carbon capture and storage (CCS) and CES coupling is constructed. Moreover, a bi-level optimization model with the upper tier subject being the PIES operator and the lower tier subject being the CES operator is established under the ladder-type carbon price mechanism with reward and punishment (LCPMRP). Then a proposed entropy weight adaptive information gap decision theory method (EAIGDT) is proposed to eliminate the subjectivity factor and retain its non-probabilistic features while dealing with multiple source-load uncertainties, and according to the operator’s risk preference to build risk-averse (RA) and risk-seeking (RS) strategies, respectively. Finally, the measured data in a certain area of Xinjiang verifies the proposed optimal scheduling method. The results show that the method can effectively take into account the interests of various subjects and realise PIES low-carbon economic operation.

An optimal dispatch strategy of off-grid park integrated energy system considering source volatility

An off-grid integrated energy system (IES) with hydrogen storage at park-level is proposed, utilizing wind, solar and natural gas as the main energy supply to replace fossil fuels, in order to overcome the insufficient consideration of energy source conversion and information exchange in the traditional energy system. Firstly, an IES schematic consisted with typical devices is proposed, and all the components are modelled in detail. Then, based on the renewable energy (RE) input requirements and the facility parameters, an optimal dispatch model for multi-energy conversion based on hydrogen storage has been demonstrated. In addition, an improved particle swarm optimization algorithm is also introduced to improve the strategy. Combined with the environmental characteristics, the multi-objective solution with the lowest economic cost and environmental cost, the highest energy supply reliability and energy efficiency as the optimization objectives is determined. Finally, a case study is conducted using the practical data of the Chongli Large-Scale Wind-Solar Complementary Coupled Hydrogen Production System Application Demonstration Project to validate the feasibility of the study under real conditions. The result shows that the RE consumption rate is greater than 99 % under the premise of considering the environmental cost and ensuring the operation reliability, which can effectively improve the operation economy and user economy of the power grid

Low-carbon optimal scheduling of park-integrated energy system based on bidirectional Stackelberg-Nash game theory

Multi-stakeholder participation is crucial in facilitating the development of park-integrated energy systems (PIES). Balancing the diverse interests of various stakeholders, each with its distinct requirements presents a notable challenge. Concurrently, the model's complexity increases due to the engagement of various stakeholders, posing challenges to its resolution through traditional methods. In this context, this paper aims to investigate an optimal scheduling model that incorporates shared energy storage (SES) system, microgrids operator (MGO), electric vehicles station (EVS), and user aggregator (UA) with multiple prosumers. To comprehensively address the interests of all stakeholders, this study introduces a tri-level optimization model. The proposed model integrates a bidirectional Stackelberg-Nash game framework, in which the SES acts as the leader, the MGO acts as the secondary leader, and the UA-EVS acts as the followers while allocating benefits based on the asymmetric Nash bargaining theory. The Stackelberg game model between MGO and UA-EVS is analyzed using the Karush-Kuhn-Tucker (KKT) condition, while the Stackelberg game model between SES and MGO is resolved using the bisection method. Meanwhile, the Nash bargaining method among users is solved using the alternating direction method of multipliers (ADMM) technique. The analysis indicates that the proposed strategy can reduce PIES's costs and carbon emissions, yielding a win-win situation for all stakeholders.

Economic Optimal Scheduling of Integrated Energy System Considering Wind–Solar Uncertainty and Power to Gas and Carbon Capture and Storage

With the shortage of fossil energy and the increasingly serious environmental problems, renewable energy based on wind and solar power generation has been gradually developed. For the problem of wind power uncertainty and the low-carbon economic optimization problem of an integrated energy system with power to gas (P2G) and carbon capture and storage (CCS), this paper proposes an economic optimization scheduling strategy of an integrated energy system considering wind power uncertainty and P2G-CCS technology. Firstly, the mathematical model of the park integrated energy system with P2G-CCS technology is established. Secondly, to address the wind power uncertainty problem, Latin hypercube sampling (LHS) is used to generate a large number of wind power scenarios, and the fast antecedent elimination technique is used to reduce the scenarios. Then, to establish a mixed integer linear programming model, the branch and bound algorithm is employed to develop an economic optimal scheduling model with the lowest operating cost of the system as the optimization objective, taking into account the ladder-type carbon trading mechanism, and the sensitivity of the scale parameters of P2G-CCS construction is analyzed. Finally, the scheduling scheme is introduced into a typical industrial park model for simulation. The simulation result shows that the consideration of the wind uncertainty problem can further reduce the system’s operating cost, and the introduction of P2G-CCS can effectively help the park’s integrated energy system to reduce carbon emissions and solve the problem of wind and solar power consumption. Moreover, it can more effectively reduce the system’s operating costs and improve the economic benefits of the park.

Optimization and scheduling scheme of park-integrated energy system based on multi-objective Beluga Whale Algorithm

To improve the consumption rate of renewable energy and ensure the stability of power and heat supply within the industrial park, a park-integrated energy system (PIES), which incorporates electric heating equipment, energy storage devices, and multiple micro sources, is established. Based on the established PIES, a price-based demand response (PDR) mechanism is introduced to establish a multi-objective optimization and scheduling model with two time scales: day-ahead scheduling and intra-day scheduling, aiming to minimize the impact on the main grid and ensure the flexibility of system operation and real-time scheduling. With the objectives of minimizing operating costs and maximizing environmental benefits for PIES, a hybrid algorithm named Non-Dominated Sorting Beluga Whale Optimization (NSBWO), which combines the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Beluga Whale Optimization (BWO) algorithm is proposed to solve the optimization problem of the model. Simultaneously, an adaptive penalty function is introduced in the algorithm to handle constraint problems in optimization and additional penalty term for wind and solar curtailment is included within the constraints to improve the consumption rate of sustainable energy. The proposed NSBWO algorithm exhibits more robust search capabilities and faster convergence speed than the NSGA-II and MOPSO algorithm. Simulation results show that the proposed NSBWO algorithm can achieve low-carbon economic scheduling of the system in both long-term and short-term time scales and improve renewable energy consumption. Due to its flexibility, intra-day scheduling has lower operating costs than day-ahead scheduling.

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.

Optimized Operation of Park Integrated Energy System with Source-Load Flexible Response Based on Comprehensive Evaluation Index

Optimized Operation of Park Integrated Energy System with Source-Load Flexible Response Based on Comprehensive Evaluation Index

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.