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To better reduce the carbon emissions of a park-integrated energy system (PIES), optimize the comprehensive operating cost, and smooth the load curve, a source-load flexible response model based on the comprehensive evaluation index is proposed. Firstly, a source-load flexible response model is proposed under the stepped carbon trading mechanism; the organic Rankine cycle is introduced into the source-side to construct a flexible response model with traditional combined heat and power (CHP) unit and electric boiler to realize the flexible response of CHP to load; and the load-side categorizes loads into transferable, interruptible, and substitutable loads according to the load characteristics and establishes a comprehensive demand response model. Secondly, the analytic network process (ANP) considers the linkages between indicators and allows decision-makers to consider the interactions of elements in a complex dynamic system, resulting in more realistic indicator assignment values. Considering the economy, energy efficiency, and environment, the PIES optimization operation model based on the ANP comprehensive evaluation index is constructed to optimize the system operation comprehensively. Finally, the CPLEX solver in MATLAB was employed to solve the problem. The results of the example show that the source-load flexible response model proposed in this paper reduces the operating cost of the system by 29.90%, improves the comprehensive utilization rate by 15.00%, and reduces the carbon emission by 26.98%, which effectively enhances the system's economy and low carbon, and the comprehensive evaluation index based on the ANP reaches 0.95, which takes into account the economy, energy efficiency, and the environment, and is more superior than the single evaluation index. [ABSTRACT FROM AUTHOR]

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Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The technology of power to hydrogen (P2H) and natural gas mixed hydrogen is highly regarded in theoretical research and engineering application in promoting the consumption of renewable energy and reducing carbon emissions. Targeting the park integrated energy system with high proportion renewable energy, an optimal configuration method of P2H considering natural gas mixed with hydrogen and cross seasonal storage is proposed. Firstly, the operational framework and energy flow relationship of the park integrated energy system are sorted out, and the mathematical models for the internal energy production, conversion, and storage equipment in the park are established. Secondly, a configuration model of P2H is established with the goal of optimizing the annual investment cost of equipment, the annual operating cost, and the carbon trading cost. Finally, the effectiveness of the proposed model is verified by an example analysis. The impact of changes in investment cost of electrolytic cells, the upper limit of mixed hydrogen volume fraction and cost weight coefficients of economy and low-carbon on the planned operation results are analyzed. The simulation results show that the proposed model can effectively improve the absorption capacity of renewable energy and reduce the overall economic cost and carbon emissions.

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

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. [ABSTRACT FROM AUTHOR]

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.

Under the requirements of achieving carbon peak and carbon neutralization in China, the development of hydrogen energy industry has become an important goal of China's future development because hydrogen energy has the characteristics of zero carbon emission and high calorific value. In order to solve the characteristics of low energy conversion efficiency, small amount of hydrogen production and low economic benefit of the comprehensive energy system of the industrial park for hydrogen production and energy storage by electrolytic water, as well as the national need for the development of large-scale and low-cost hydrogen production by hydrogen energy, a (park-level intergrated energy system, PIES) of the hydrogen storage park with natural gas hydrogen production mode instead of electrolytic cell hydrogen production was proposed. Based on the analysis of energy consumption and energy recovery of hydrogen production unit by steam reforming of natural gas and various energy characteristics of electricity, heat and gas of hydrogen energy storage unit, a hydrogen energy storage model for hydrogen production and energy storage of natural gas was established. Further considering the equipment investment cost, operation cost, carbon emission price and hydrogen production benefit, the objective function was established with the minimum annual investment cost and the maximum annual operation benefit as the objective, which is solved by fast non dominated sorting genetic algorithm (NSGA-II), and the hydrogen storage optimal allocation model of PIES based on hydrogen production and storage of natural gas was proposed. Finally, taking a park in Xinjiang as an example, the characteristics of power supply, electrical load, and heat load, operational economic benefits, and the impact of natural gas price and carbon emission price on the configuration of the park after hydrogen energy storage in the park were analyzed. The feasibility of improving the hydrogen production capacity and operation income of the PIES by configuring hydrogen storage for hydrogen production from natural gas were verified, and the applicable scenarios were pointed out. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the opening of the Chinese electricity market, as a retailer that provides energy services to consumers, the park-integrated energy system (PIES) not only serves as an effective way to earn benefits and reduce carbon emissions but also impacts the energy consumption characteristics of consumers. The PIES implements this function by adjusting the energy selling price in free energy markets. The pricing mechanism model (P-M model) is established to obtain the energy selling price in the planning and design stages. In this model, the impact of the demand response on the energy configuration and the impact of the changes in energy configuration on the energy cost price are both considered. Additionally, the optimal result ensures that both the consumers and the PIES benefit simultaneously. The reactive demand response zone, which represents a consumer trap, is found in numerical studies. The results indicate the following: (1) from the perspective of P-M model optimization, the benefit exclusive point of the PIES is the optimal solution in the short term; (2) from the perspective of the long-term benefit, the ultimate result in the relationship between the PIES and consumers is that the PIES will share its profits with consumers; in other words, benefit sharing point is the optimal solution for the long term. [ABSTRACT FROM AUTHOR]

With the opening of the Chinese electricity market, as a retailer that provides energy services to consumers, the park-integrated energy system (PIES) not only serves as an effective way to earn benefits and reduce carbon emissions but also impacts the energy consumption characteristics of consumers. The PIES implements this function by adjusting the energy selling price in free energy markets. The pricing mechanism model (P-M model) is established to obtain the energy selling price in the planning and design stages. In this model, the impact of the demand response on the energy configuration and the impact of the changes in energy configuration on the energy cost price are both considered. Additionally, the optimal result ensures that both the consumers and the PIES benefit simultaneously. The reactive demand response zone, which represents a consumer trap, is found in numerical studies. The results indicate the following: (1) from the perspective of P-M model optimization, the benefit exclusive point of the PIES is the optimal solution in the short term; (2) from the perspective of the long-term benefit, the ultimate result in the relationship between the PIES and consumers is that the PIES will share its profits with consumers; in other words, benefit sharing point is the optimal solution for the long term.

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. • A multi time-scale optimization scheduling model for PIES is established. • Demand response comprehensive incentive mechanism is proposed. • Stackelberg game model is established to guide users to transfer part of power load. • Different demand response measures are considered at different time scales. • Considering the uncertainty of power source and load, simultaneously. [ABSTRACT FROM AUTHOR]

• Our model can divide the planning cycle into multiple phases, closely following the actual development process of the park. • Our model takes uncertainties of renewable energy and loads into account via robust optimization. • Our model can be decomposed into the master problem and the sub-problem using the C&CG. • The economy and superiority of the model are verified via case studies and analyses. The promotion and construction of park integrated energy system (PIES) has strengthened the interaction among electric, gas, heating and cooling systems. However, the static programming strategy (SPS) of PIES results in problems such as lower system economics and poorer reliability of energy supply. Therefore, a suitable planning method is required to promote the economic operation of PIES under multiple uncertainties. Based on the background, this study proposes a two-stage robust optimization model based on dynamic programming strategy (DPS) for PIES. Firstly, the coupled devices and energy networks in the system are analyzed in detail to comprehensively identify the complex interactions among multiple energy carriers. Moreover, a DPS is introduced to improve the economic efficiency of PIES. Then, a two-stage robust model is established to solve the heterogeneous uncertainties in the planning strategy. Specifically, the column and constraint generation (C&CG) algorithm is used to solve the optimization model thereby obtain the equipment configuration scheme for each planning phase. Finally, the effectiveness of the proposed method is verified by comprehensive case studies on a test system. [ABSTRACT FROM AUTHOR]

• A two-layer game interaction framework is constructed. • The upper layer is a Stackelberg game, and the lower layer is a cooperative game. • A two-layer decision optimization model is constructed. • The Stackelberg game is based on maximum net income as the objective function. • The income allocation model is constructed in the lower layer. Due to the reform and liberalization of the multi-energy market and the increasing complexity of the internal main body coupling of the park integrated energy system, the effective use of the external multi-energy market and the flexible participation of internal multiple subjects to compensate for the shortage of supply in the park integrated energy system has practical research significance. Therefore, in this paper, a two-layer game model was constructed to optimize the park integrated energy system internally and externally. Firstly, an upper-level Stackelberg game model of the superior energy network and park system was constructed to carry out external optimization of the park integrated energy system. Second, a cooperative game model was constructed for the park users, the gas supply system, and the park integrated energy system to undertake internal optimization of the park integrated energy system. In addition, a multi-agent cooperative game benefit distribution model was constructed based on the Shapley value method, the Banzhaf value method, the solidarity value method, and the improved risk factor. Finally, multiple scenarios were established and the solution was optimized using a two-stage solution algorithm. The results show that cooperation between users, the gas supply system, and the park integrated energy system of the lower-level park can improve the internal supply and demand matching of the park integrated energy system, and enhance its competitiveness in the upper Stackelberg game. [ABSTRACT FROM AUTHOR]

The Park Integrated Energy System (PIES) integrates many types of energy, such as cooling, heating, electricity, and gas. It is an effective way to improve energy efficiency and increase clean energy consumption. However, the fluctuation of users' load demands and the complexity of the system structure limit the stable development of the PIES. Against this background, this paper introduces the Time-Of-Use (TOU) energy price and benefit allocation into the PIES, guiding users to use energy reasonably and strengthen the cooperation on the supply side. Firstly, the TOU energy price and energy policy models are constructed after constructing the PIES, which is divided into three centers: Internal Energy Supply Center (IESC), Internal Backup Energy Supply center (IBES), and Energy Conversion Center (ECC). Secondly, the first-stage operational optimization model with the objective of maximum revenue of the PIES is built. Thirdly, the second-stage benefit allocation optimization model is constructed, and it is based on an improved Shapley method. Finally, a multi-scenario case study is conducted. The results show that the TOU energy price can enhance the supply-demand match degree, improving the efficiency of energy utilization; and the improved benefit allocation model makes the revenue of participants more consistent with their contributions. • Dividing the PIES into energy supply, backup energy supply, and energy conversion. • Constructing TOU energy price, which is involved in power, cooling and heating. • Introducing the TOU and energy policy, the first-stage optimization is conducted. • Constructing the improved Shapley method, the second-stage optimization is conducted. [ABSTRACT FROM AUTHOR]

Power-to-gas (P2G) technology is considered as a new approach for clean energy consumption and energy conversion. However, because this technology must be combined with other energy systems to build a stable energy system, a reasonable income distribution method is necessary to guarantee this integration. Firstly, the operation optimization model of the park integrated energy system (PIES) and park independent energy system (PINES) with P2G are constructed for the first stage optimization, with the objective of maximizing net income. Secondly, the energy system performance evaluation indicators are developed to assess the system quantitatively in terms of the economic and environmental aspects. Thereafter, the income distribution models based on Shapley value and the improved Shapley value with operational risk factor are created, and the total income is optimally distributed at the second stage based on the first-stage optimization results. Finally, an industrial park in a province of China is selected for case analysis. The results show that (1) PIES can complement different systems, integrate the demands in heat, electricity, and gas, while realizing the electricity-gas-heat/electricity conversion and heat-electricity complementarity. (2) Income distribution using an improved Shapely value method is proposed, it overcomes the one-sidedness of transaction volume and lack of differences among participants, reflects the actual operational risk and the degree of contribution of participants to the whole system, and promotes the incentives of cooperation. (3) Based on the improved income distribution model, the income of the PS, P2G, and HS are redistributed. The income of P2G increased by ¥7,450, which reflects the important collaborative value of P2G. The willingness of system cooperation increased by 742.392%. Therefore, the proposed operation optimization and income distribution model can enhance the incentives of participants want to cooperate under the premise of ensuring the maximum net income of the PIES. Moreover, it can be used as reference for the formulation of an optimal operation plan and income distribution of the complex energy system and it can provide a way to promote clean energy production. • The role of P2G as a new energy conversion way is studied. • The two-stage optimization model is constructed. • The first stage is operational optimization. • The second stage is income distribution. • The improved Shapley value method is applied in park integrated energy system. [ABSTRACT FROM AUTHOR]

• The park integrated energy system is constructed. • The multi-energy demand response model is constructed. • The performance evaluation indexes are constructed. Multi-energy demand response is an important means to achieve peak load shifting, and improve energy efficiency. It plays a significant role in promoting the sustainable development of park integrated energy system. In this context, we first constructed a multi-energy demand response model from the perspective of an elastic matrix. Second, we established performance evaluation indexes of the park integrated energy system based on economic and environmental factors, to match energy supply and demand. We then constructed an optimization model considering the unit output, energy balance, and other constraints to achieve optimal net profits, energy utilization efficiency, and degree of matching between load and output. Finally, we established multiple scenarios and used the multi-objective particle swarm optimization algorithm combined with fuzzy set theory for an example analysis. The results show that after redesigning the cooling, heating, and electricity prices, the multi-energy demand response model transfers the user load demand, without changing the total energy consumption. Compared with the traditional scenario, the energy utilization efficiency and net system profit increased by 2.30 % and $2,652.775, respectively, while the user expenditure decreased by $7,663.887, following implementation of the demand response model. This demonstrates win-win situations for the economy/environment and for the system/users. [ABSTRACT FROM AUTHOR]