With the rapid development of hydrogen production and storage technology, the development of hydrogen energy storage systems (HESSs) will bring fundamental changes to energy and power system structure. The coordinated optimization of HESS and battery energy storage system (BESS) can solve the imbalance between supply and demand of various energy sources and improve energy efficiency. In order to ensure the effectiveness of BESS and HESS planning, minimum life cycle cost (LCC), system network loss, switching power deviation, load fluctuation, and voltage fluctuation are chosen as the fitness function in this paper. Meanwhile, a non-dominated sorting genetic algorithm-II (NSGA2) with elite strategy is used to solve energy storage system (ESS) Pareto non-dominated solution set of site-constant volume planning scheme. The grey target decision based on entropy weight method (EWM) is used to select the best compromise solution in Pareto non-dominated solution set. Additionally, typical operation scenarios of source load are obtained by fuzzy kernel C-means (FKCM) clustering algorithm, and the simulation analysis is carried out on the basis of the extended IEEE 33-node system. Simulation results show that NSGA2 algorithm not only achieves the minimum LCC of the electricity-hydrogen hybrid energy storage system, but also improves voltage quality, power stability, network loss and load fluctuation compared to that of other algorithms.
In order to study the influence of hybrid energy storage system with fast frequency response on the dynamics of the new power system, and find the best control strategies for different scenarios, a generalized dynamic model of the hybrid energy storage system is proposed. On the basis of this model, the PI, H∞ and sliding mode controllers for the hybrid energy storage system consisting of battery and flywheel energy storage are designed. A case study serves to compare the different control strategies of the hybrid energy system. The results show that the combination of battery with H∞ control and flywheel with sliding mode control has the best frequency support capability.
According to the complementary characteristics of electrochemical energy storage and hydrogen storage, an integrated optimization model for the configuration and operation of a hybrid energy storage system is given, including electrochemical energy storage, hydrogen storage proposed and an intelligent algorithm. The model is based on a two-layer decision optimization problem, in which two different time dimensions of the hybrid energy storage system configuration and operation are solved in upper and lower layers, and the interaction between them is considered. A reinforcement learning proximal policy optimization (PPO) algorithm is used to solve the two-layer optimization model. By comparing the results of applying various traditional algorithms to solve the scenery data of a region in Gansu Province, it is verified that the used algorithm has the highest adaptability and the fastest convergence speed in a complex environment. The results show that the application of this model can reduce the abandoning rate of wind and solar power by 24% and effectively improve the comprehensive benefit of the system, and that hydrogen storage as a capacity-based energy storage configuration is not limited by topographical factors and is suitable for diverse application scenarios, thus providing an application demonstration for the widespread deployment of hydrogen storage, a new form of energy storage, in the whole country.
Reliability of power system is facing severe challenges due to the full accommodation of renewable energy. In this paper, on the basis of the long-term reliability assessment of north American bulk power system, the main factors affecting the reliability of power grid are discussed, and the influence of renewable energy on power system reliability is analyzed. Finally, according to current situation of power system planning and operation in China, suggestions are put forward for improving the reliability standard, integrated design of power grid and full accommodation of renewable energy.
The wide access of the high proportion of power electronic equipment and the high proportion of distributed photovoltaic power, and the improvement of the urban cabling rate make the reactive power characteristics on the user side of the distribution network complicate. The increased uncertainty of the load reactive power consumption is not conducive to the safe operation of distribution network. Therefore, to better optimize reactive power, the combined scenarios and their probabilities of daily power-factor variation curves of different loads are used to reflect the uncertainty of reactive power. Taking the minimum expected value of operation cost as the objective function, an optimal configuration model for the expected value of multiple reactive power scenarios is established. Firstly, multiple one-dimensional convolutional autoencoders (1D-CAEs) is used to extract the low-dimensional representation of the daily power factor data of different users. Then, the k-means method is used for scene reduction to obtain typical daily power-factor variation scenes, and multi-user scenario set is combined. Finally, the expected value reactive power optimization model is established, and the particle swarm algorithm is used to solve it to determine the optimal configuration scheme. According to the reactive power consumption scenarios of users in a distribution network in Shanghai, the modified IEEE 33-node system is taken as an example to verify the effectiveness of the proposed method.
The remote mountainous areas at the end of the distribution network are often regarded as weak areas of the distribution network due to insufficient power supply reliability. In order to improve the power supply reliability of such areas, the concepts and methods of virtual substation and cluster planning are introduced, and a bi-level location and capacity planning model based on virtual substation for distributed power sources and energy storage is proposed. The upper-level model takes the largest cluster efficiency index as the goal, and divides the weak areas of the distribution network into clusters from both structural and functional aspects, whose planning result is the division plan of each cluster. The lower-level model aims to minimize the annual comprehensive cost considering the user’s loss costs, and build virtual substations within each cluster, whose results are the access location and capacity of distributed power sources in the cluster, and the access capacity and power of energy storage devices. Focusing on the characteristics of the cluster planning model, a cost calculation method for users’ power outage loss considering island operation is proposed, and an iterative hybrid particle swarm algorithm is used to solve the problem. A typical scene with distributed small hydropower stations in Yunnan is taken as an example to verify the feasibility of the proposed model and the effectiveness of the solution method.
After a major power outage, using the local distributed generators of the urban distribution system to restore critical loads is an effective method to mitigate outage and improve the resilience. The loads in urban power grid include the customer loads that can only be functioned with the joint support of various resources such as electricity, water and gas, as well as the critical infrastructure loads such as traffic lights and water pumps. Besides, there exists closed interdependency among power grid, customer loads, and critical infrastructure loads. In this paper, a multi-period restoration method for distribution system critical loads considering the load function requirements is proposed. On the load side, the power, water, and gas demand of customer loads and the power demand of traffic loads are considered. On the grid side, the operation constraints of power distribution system, water network, and gas network are considered. On the source side, the flexible access of mobile resources and limited resources of local distributed generators are considered. The results of case studies show that the proposed method can maximize the social function of various loads and realize the optimal allocation of limited generation resources.
Active distribution network is gradually replacing traditional distribution network, which has the characteristics of flexible dispatching and control, high user interaction and high energy utilization. It achieves the goal of economic optimization under the premise of safe operation of the system. A hierarchical optimal dispatching method based on ADMM algorithm for active distribution network is proposed. Firstly, the hierarchical optimization dispatching model of active distribution network is established with the goal of minimizing the overall operating cost of distribution network. With the help of ADMM algorithm, the model is decomposed into two layers for solving. The upper layer is solved with the goal of minimizing the overall operating cost of distribution network. The lower layer is to reduce the costs of local energy storage operation and power purchase. The upper and lower layers iterate with each other through limited boundary information exchange until the convergence condition is satisfied and the optimal solution is obtained. Finally, an example is designed to test the effectiveness and feasibility of the proposed scheduling method.
Considering that the output of photovoltaic and wind power is complementary in time, and that the energy storage devices may provide bidirectional power flow, the renewable energy and storage devices are often connected to the distribution grid in the form of microgrid to realize the high proportion of renewable energy access. The static voltage stability index of the existing distribution grid is improved, and the static voltage stability and operation economy of the distribution network are used as the target to study the location and capacity determination of the energy storage in the microgrid. The multi-scenario technology based on K-means++ method is used to deal with the uncertainty of renewable energy output, and the optimal capacity allocation ratio of three types of scenery storage devices is calculated for the renewable energy output characteristics of the distribution network in different areas. The equilibrium optimizer (EO) algorithm is improved by using chaotic sequences generated by Tent mapping instead of randomly generated initial populations to achieve multi-objective problem solution according to congestion and non-dominated ranking. The effectiveness of the model and algorithm in this paper is verified by simulation in the IEEE 33-node and PG&E-69 system.
In view of the problem that the existing research on reliability of modular multi-level converter (MMC) does not fully consider time-varying failure rate of redundant system, and lacks the evaluation method for renewal process, which can not scientifically measure the reliability of MMC, this paper proposes an MMC reliability modeling method considering the time-varying failure rate and N-step renewal process of redundant system. Firstly, from the perspective of redundancy degree and redundancy mode, the classification of redundant systems is refined. According to the classification above, the mathematical analytical relationship between reliability and failure rate is established, which indicates the characteristics of the time-varying failure rate of redundant system. It is pointed out that the failure rate of redundant system obtained by traditional methods is too small. Then, N-step failure rate is used to replace the time-varying failure rate of MMC redundant system. At the same time, the renewal process theory is introduced to deduce the mean time to failure (MTTF) and steady-state availability formula of MMC, so as to correct the problem that the calculation results of traditional methods are too high. Finally, the converter of half-bridge sub-module is taken as an example to verify the effectiveness and feasibility of the proposed method.
As a popular application of DC converter technology, dual active bridge (DAB) has received more and more attention from scholars. The reflow power is one of the research hotspots. Excessive return power will seriously affect the operating efficiency of the DAB and reduce the soft-switching range of the DAB. In view of the above problems, on the basis of the extended phase-shift control, this paper determines the boundary conditions that can realize the soft switching of all switches, which effectively reduces the switching loss. Even zero return power can be achieved under certain operating conditions, and the operating efficiency of DAB has been improved. For on-line control, a closed-loop control system is designed, and the effectiveness of the method is proved by the continuous switching simulation of the load. Finally, the DAB experimental platform is built, and the correctness of the theory is verified by hardware.
The grid-connected consumption of "green and low-carbon" new energy such as large-scale wind power and new photovoltaic power urgently needs the development and research on new flexible DC transmission technology and equipment. On the basis of the test and demonstration project of Zhangbei DC power transmission, this paper systematically studies the core equipment of hybrid DC circuit breaker. According to the working principle of hybrid DC circuit breaker and the performance requirements of fast mechanical switch, the high-voltage fast mechanical switch is proposed. Through the finite element simulation of the dynamic characteristics of fast mechanical switch, it shows that the fast mechanical switch can move to the transient voltage insulation distance within 2 ms, and the simulation results are verified by electrical performance test. The successfully developed high-voltage and high-current fast mechanical switch has been put into practice in Zhangbei HVDC transmission test and demonstration project.
The existing incomplete mathematical model, incomplete working mode, single optimization goal and inability to optimize online are the main obstacles for further improving the performance of extended-phase-shift (EPS) control strategy on dual active bridge (DAB) converter. The whole EPS working modes are analyzed and the mathematical models of transmission power, backflow power and current stress of each mode are established. On the basis of the integrated mathematical models, a compounded EPS optimization control strategy for minimizing backflow power and current stress is proposed. Specifically, the goal is to optimize the current stress under the premise of ensuring the minimum backflow power. For this goal, some appropriate modes are selected according to the analysis and comparison of the performance of each mode. After selecting the better modes, the constrained extreme value solution method is adopted to further obtain the optimal control path. A closed-loop controller is designed. Finally, the proposed control strategy is verified through experiments. The experimental results show that, the lowest backflow power and lower current stress of the DAB converter could be ensured under the proposed control strategy. Consequently, the efficiency is enhanced.
The integration of distributed generation, energy storage and flexible load endows the demand side with the ability of flexible regulation and control, enabling it to share electricity as a prosumer, in order to promote the optimal allocation of power resources. Therefore, this paper studies the transaction mechanism of power sharing market, and proposes a distributed trading strategy based on value recognition mechanism for power sharing at demand side, aiming to reduce the transaction cost of power market and improve the market efficiency. Firstly, the electricity sharing mode driven by marginal price is designed according to surplus theory, and the market game model is established on the basis of best reaction function, which reveals the meaningless loss caused by disorderly competition in the market. In this regard, a value recognition mechanism is proposed to improve the operation efficiency of power sharing market, and a distributed transaction strategy based on consistency algorithm is designed to realize decentralized transaction among prosumers, so as to protect the privacy of users. Finally, numerical simulation shows that the proposed trading strategy can achieve Pareto improvement of electricity sharing market and promote the optimal allocation of power resources.
In the "double carbon target" background, the coordinated operation of the new power system by implementing demand-side response is the main means to solve the uncertainty of new energy output. However, in reality, customers have bounded rationality, and their response to tariffs or incentives is heterogeneous and uncertain. In view of this, considering the information interaction and strategy learning characteristics of bounded rational users in the response process of electricity price or incentive measures, this paper proposes a user-side demand response analysis model based on evolutionary game model in social network. Firstly, considering that the real economic man has information interaction and strategy learning and updating in decision-making, and that the information interaction is a complex community system, the information interaction relationship between users is described by constructing a social network. Secondly, according to the electricity price theory and the balance between power supply and demand, the response decision of a single user will affect the interests of other users. The game model is used to describe the decision-making process of group users. Finally, considering the bounded rationality of users’ information processing, the evolutionary game model in social network is used to describe the response process of users to electricity price or incentive measures. The effects of different social network structures and electricity prices or incentives on user response are simulated and analyzed. The results show that the enhancement of the small world attribute of users’ social network will improve users’ response, and the increase of price coefficient will reduce users’ willingness to use electricity.
