Integrated energy system (IES) is an important development direction of energy consumption in the future, and will play an increasingly important role in improving the energy efficiency of integrated energy use and the consumption of renewable energy. However, due to the high coupling of energy system in IES and the uncertainty in each link of source-network-load-storage, the dynamic process of IES is extremely complex, which brings great challenges to the optimal operation of IES. This paper aims at the optimal operation of IES. Firstly, the concept of IES is introduced and its main characteristics are analyzed. Secondly, the research status in home and abroad on the basic information perception of IES operation, the dynamic coordinated optimization scheduling system with hybrid time scales and its solution algorithm are summarized, and the shortcomings of the existing research are pointed out. Finally, the future research direction is prospected. The results of this paper will provide a reference for the follow-up research of integrated energy system.
Zero-carbon park is one of the key points in the practice of carbon neutralization and emission peak. However, in the context of the new power system, the electric energy supply of the park is characterized by the high integration of water, electricity, gas and heat, strong uncertainty of source-grid-load-storage, and complex and diverse primary and secondary topologies, which makes it difficult for the existing planning and design technologies to meet the application requirements. To this end, this paper discusses the current status, key technologies and future development trends of the optimal planning and design of zero-carbon parks. Firstly, the current status of research on zero-carbon parks and their planning and design technologies is explained from the perspective of energy interconnection. Secondly, the key technologies for optimal planning of zero-carbon parks, including energy-transportation system coupling, multi-energy complementarity, energy gradient utilization, demand response, and progressive planning, are summarized and explained. Finally, the future development trend of park planning is analyzed in terms of sharing concept, digital twin and cloud energy storage, and the technical challenges and research directions of park planning are indicated from the perspectives of concept application, disciplinary intersection, information integration and situational awareness, in order to provide stronger technical support for the development of zero-carbon parks for energy internet in China.
Under the dual carbon target, it is imperative to develop integrated energy system planning and research, and the flexible loading, unloading and configuration of the user-side integrated energy module is one of the emerging research objects. In this paper, a two-stage integrated energy planning model is established with the introduction of multiple hybrid energy storages and carbon capture devices, which achieves the complementary use of energy and carbon emission recovery and utilization in the system. To deal with new energy output such as photovoltaic and wind power, and user load uncertainty, extreme scenario is proposed. According to ellipsoid set data driven method of robust optimization in uncertainty, the paper accurately describes the correlation between variables and improves the conservation of traditional robust optimization result. Compared with the traditional method with mass probability calculation, a simpler ellipsoid endpoint extraction method is used to obtain extreme scenes. In this paper, the steps of column and constraint generation (CCG) solution are improved by using the advantage of ellipsoidal extreme scenarios to avoid the complicated duality processing of sub-problems. Finally, by example simulation and comparison with the traditional interval uncertain set robust optimization method, it is proved that method proposed in this paper has advantages in reducing economic cost, energy saving and reducing carbon emissions.
In order to overcome the information barrier of power-transportation system and improve the low utilization rate of renewable power generations, a distributed planning model of renewable vehicle energy supply system considering seasonal hydrogen storage is proposed in this paper. Firstly, an energy supply architecture including winter-summer cross-seasonal hydrogen storage is proposed, and the operation behaviors of distribution network, combined energy supply station, and transportation network are modeled. Secondly, an electric-transportation joint planning model is established with the goal of economy. Then, a tailored alternating direction multiplier methool (ADMM) is proposed to realize the distributed solution of energy supply system and transportation system. Finally, the validity and advancement of seasonal hydrogen storage model and distributed algorithm are verified based on IEEE 33-node power distribution network and 12-node typical transportation network, and the synergies among various storages are discussed.
Energy internet-oriented zero-carbon park is dominated by new energy sources, bringing together a high percentage of renewable energy sources such as wind, biomass and solar energy, hydrogen generation, coal power and other forms of energy. However, there is little research on data state sensing of equipment in zero-carbon parks. In order to reasonably plan intelligent sensing devices for data collection and analysis in zero-carbon parks and ensure the reliable, safe, high-quality, low-carbon and economic operation of energy systems in zero-carbon parks, this paper proposes an energy Internet-oriented optimal planning method for intelligent sensing devices in zero-carbon parks. Firstly, the paper analyzes the requirements of state sensing devices in zero-carbon parks, formulates the principles of intelligent sensing device optimization planning, considers the investment cost, maintenance cost and failure cost, and proposes a mathematical model for intelligent sensing device optimization planning in zero-carbon parks. Secondly, in order to realize the accurate solution of the formulated mathematical model, the paper proposes a grey wolf and teaching-learning hybrid optimization (GWO-TLBO) algorithm. Finally, a practical case of a zero-carbon park is used as a simulation example to verify that the proposed intelligent sensing device optimization planning method for zero-carbon park can significantly reduce the life-cycle cost. The comparison experiments with existing intelligent algorithms show that the proposed GWO-TLBO has the highest solution accuracy.
The increase of the proportion of renewable energy will reduce the flexibility, increase the economic cost, and have an impact on the stability of power grid operation. With the expansion of the scale of electric vehicles, their large-scale access to the power grid will also affect the stability of the power grid due to charging uncertainty. The implementation of vehicle to grid (V2G) technology makes it possible for electric vehicles to participate in peak-shaving auxiliary services on a large scale, so it should be included in the future power system planning. Aiming at large-scale electric vehicles participating in peak shaving, this paper focuses on the influence of seasonal factors on electric vehicles participating in V2G output. In the research process, a multi-objective programming model is established to minimize the system operation cost, minimize the load fluctuation on the grid side and maximize the economic benefits on the user side, so as to optimize the power supply structure, reduce the carbon emission on the power supply side and improve the overall economic benefits of the system. Taking Hebei Province of China as an example, different scenarios are set for research and analysis. The results show that, when the participation ratio of V2G is 70% in the planning period, the result is the best, and the carbon emission on the power side is effectively reduced by 3.45%. The consumption of wind and solar energy can be increased by 10.18%, promoting the transformation of power structure.
In the context of low-carbon distribution network, this paper proposes a mixed-integer second-order cone programming model for active distribution networks considering carbon emissions and flexible loads, with the investment strategy with minimum total cost. Considering the uncertainty of renewable energy, load and energy price, a scenario clustering method based on K-means is proposed. The decision variables of the model are the replacement of overloaded lines, the construction of new energy and energy storage devices, and the construction of voltage control equipment such as voltage regulators and capacitor banks. The polynomial voltage-dependent flexible loads, network reconfiguration and carbon emission constraints are considered. Aiming at the non-convex nonlinear characteristics of the planning model, the virtual demand method is used to model the network reconstruction as a mixed integer linear programming form, and an improved second-order cone relaxation method based on Taylor expansion is proposed to solve the problem of traditional second-order cone relaxation caused by flexible load model. The model is tested with a 69-node system, and the results show that the proposed model not only has a lower overall planning cost, but also helps reduce carbon emissions.
In order to successfully achieve the goal of carbon peaking and accelerate the process of carbon emission reduction, a carbon quota mechanism of the provincial power generation industry under the background of carbon peaking is designed in this paper. According to the documents issued by the government to convey emission reduction targets, a model of total annual quotas in the context of carbon peaking is established, and a monthly quota model of the power generation industry is constructed on the basis of the proportion of carbon emissions in the industry. A reasonable quota mechanism is established to issue the total amount. A distribution method that combines paid quotas and gratuitous quotas is designed. The responsibilities and specific processes of each participant in the quota determination and issuance process are clarified. Finally, taking Shandong Province as an example, the simulated unit system is used to simulate the designed distribution mechanism to verify the implementability and effectiveness of the proposed model and mechanism.
Multi-park integrated energy system can significantly improve the operation economy by complementing each other with multiple energy sources. However, the complex interactions between parks and multi-energy coupling decisions can bring challenging problems such as large decision space and difficult convergence of algorithms to the energy management of multi-park integrated energy system. To solve the above problems, an energy management method based on modified deep Q network (MDQN) algorithm for multi-park integrated energy systems is proposed. Firstly, the external meteorological data and historical interactive power data independent of the park are used to construct a long short-term memory (LSTM) deep network-based external interactive environmental equivalence model for each park integrated energy system, which reduces the computational complexity of the reinforcement learning reward function. Secondly, an improved DQN algorithm based on k-first sampling strategy is proposed to replace the greedy strategy with k-first sampling strategy to overcome the inefficiency of exploration in large-scale action spaces. Finally, the results are validated in an algorithm containing three integrated energy systems in the park, and show that the MDQN algorithm has better convergence and stability compared with the original DQN algorithm, while it can improve the economic efficiency of the park by 29.16%.
Introducing power-to-hydrogen (PtH) into offshore wind farms can assist the integration of wind power and produce green hydrogen, which accelerates decarbonization in industrial sectors and has attracted attention worldwide. In these circumstances, this paper studies the real-time energy management strategy of the offshore wind PtH microgrid. First, the real-time energy management model of the offshore wind PtH microgrid is proposed, including offshore wind farms, PtH devices and hydrogen storage tanks. Then, real-time energy management strategy based on approximate dynamic programming (ADP) is proposed. The value function is approximated by piece-wise linear functions (PLFs) to cope with uncertainties in the microgrid. Finally, the effectiveness and superiority of the proposed strategy is verified by case studies. Under the proposed strategy, offshore wind power can be consumed by PtH, achieving production and storage of hydrogen in advance. On the basis of the ideal case with perfect forecasting, the proposed strategy has an average optimization accuracy of more than 99% in real-time test scenarios with normal distribution.
Reserve can effectively deal with the risk brought by uncertainties and ensure the safe operation of park integrated energy system (PIES). However, previous studies mainly focused on the reserve capacity of generators to deal with the risk brought by uncertainties, and did not consider the reserve role of other flexible resources such as energy storage devices. An economic scheduling model of PIES is proposed in this paper, which considers the participation of energy storage devices in reserve allocation. The reserve capacity provided by electric energy storage, diesel generator and combined heat and power (CHP) is utilize to stabilize the volatility of wind and photovoltaic power. In order to balance the influence of reserve allocation on the operation safety and economy of PIES, a reserve allocation model considering renewable energy and load uncertainties is constructed on the basis of chance constrained programming (CCP). The reserve allocation model is embedded into the day-ahead optimal scheduling model to improve the rationality of day-ahead energy and reserve decision-making plan. By using discrete step transformation and stochastic simulation method, the original non-convex CCP problem is transformed into a solvable mixed integer linear programming (MILP) problem. The simulation results show that the participation of energy storage devices in reserve allocation can improve the operation flexibility of generators, thus improving the economy of system operation. By adjusting the confidence level of the upward and downward reserve constraint, the conservative degree of the park reserve decision can be changed, thus balancing the economy and reliability requirements of the system operation.
Renewable energy generation has a strong random and intermittent nature, which poses a great challenge to power system operation and control. Energy storage is one of the most important means to solve this problem. This paper focuses on the new background of zero-carbon park and the new economic model of shared energy storage, and conducts a study on the optimal dispatch and economic evaluation of zero-carbon park considering the capacity attenuation of shared energy storage. Firstly, the cost-benefit model of zero-carbon park is established, which takes the operation cost as the optimization objective. Then, an economic evaluation index of shared energy storage is put forward. Afterwards, three allocation options, namely, no energy storage, self-assigned energy storage and shared energy storage, and two battery options, namely, new battery and retired battery, are considered in the case study. The results of optimal dispatch and economic evaluation are compared with different options. Finally, the impact of shared energy storage service price on its economy is highlighted through sensitivity analysis, and the suggested values of service price are given. The results of the example verify that the economy of the shared energy storage is superior to other schemes. In addition, although the performance of retired batteries is lower than that of new batteries, the cost is far lower than new batteries, so the economy of retired batteries participating in this study case is better.
The CHP unit with fast power output adjustment can improve the operation flexibility of the electrothermal coupling system in the industrial park and promote the absorption of new energy and reduce carbon emission. The CHP dynamic constraints in the form of differential equations can describe the variation of variables in detail, and considering dynamic constraints in dispatching can grasp the operation state of units and reduce the risk of accidents. Therefore, aiming at the minimum cost of industrial parks and considering carbon trading process, a low-carbon economic dispatching model of industrial parks considering CHP dynamic constraints is established. Then, on the basis of the sequential framework, the differential algebraic equations of the simulation layer are discretized by the orthogonal configuration method on finite element, and the nonlinear programming problem of the optimization layer is solved by the improved adaptive differential evolution algorithm. The results of examples show that the orthogonal configuration method can obtain accurate results with fewer discrete points, improve the solving efficiency, and the CHP unit with fast power output adjustment can effectively improve the low-carbon economic level of the industrial park.
This paper proposes to integrate carbon emission trading into peak-shaving trading, to account for the carbon variation effects produced by thermal power peak-shaving, and proposes a multi-source low-carbon peak-shaving cost accounting method. Aiming at the uncertainty of wind power, this paper uses the information gap decision theory (IGDT) to reflect the information gap between the predicted value and the actual value of wind power, and constructs an uncertainty multi-source low-carbon peak-shaving transaction optimization model. Finally, a local power grid in northwest China is selected as the simulation system to verify the correctness and validity of the proposed model. Results show that the proposed multi-source low-carbon peak-shaving transaction model can promote the integration of wind power generation, ensure all participants obtain the cooperation incremental benefits, and establish a peak-shaving transaction plan for decision makers with different risk attitudes.
At present, the operation process of traditional energy system lacks diversified optimization methods for energy supply and demand, lacks flexible peak-regulation strategies, and is prone to pollution. However, the precise supply of virtual power plants provides a development direction for the transformation of energy architecture. This paper proposes a multi-energy collaborative system scheduling optimization model based on virtual power plant. Firstly, by introducing environmental governance subsystem cooperates with the electric-heat-gas integrated energy system to cooperate with electric-thermal energy storage devices, power to gas (P2G) equipment and energy trading markets, giving virtual power plants the more flexible information regulation means. Secondly, the paper established two different system operation modes to compare, and finally using MATLAB+CPLEX to simulate and verify the mathematical model. According to the results, compared with the operation mode of electricity-heat-gas energy system, the multi-energy collaborative control system of virtual power plant under optimal capacity configuration has better economy and environmental protection, and has a significant effect on improving the conversion rate of renewable resources, alleviating the phenomenon of abandoned wind and solar energy and excessive carbon emissions.