Currently, wind power development is mainly constrained by the phenomenon of wind power curtailment. The power to gas (P2G) technology can convert electrical energy into gas fuel, which can be used to promote the accommodation of wind power. The uncertainty of wind power may influence the decision result of the integrated system as well. In this paper, the basic structure of integrated system with P2G stations is constructed and a generation method of the scenario set is proposed, then a detailed two-stage coordinated optimal model for integrated systems with P2G stations and gas/power storage devices is presented, which includes the network and coupling device constraints. Moreover, a piecewise linear method is used to linearize the natural gas flow which makes the model into a mixed integer programming problems. At last, numerical results on the IEEE 39-bus system and a modified 6-node natural gas systems verify the effectiveness of the proposed model.
Energy management plays an important role in the operation optimization of integrated electricity-heat energy systems. However, the fluctuation of renewable energy power generation and the randomness of energy loads in the system make the energy management problem full of challenges. In order to solve this problem, this paper proposes an optimal energy management approach for integrated energy system considering the uncertainties of renewable energy and load demands. In this paper, the energy management problem of the system is expressed as a Markov decision process with unknown transition probability, and the state space, action space and reward function of the process are defined. In order to solve the Markov decision process, an optimal energy management approach based on deep Q-learning network is proposed. Simulation results show that the proposed method can adaptively respond to the random fluctuations of source and loads and realize the optimal energy management.
Interconnection and environmental friendliness are important characteristics of regional energy system and inherent requirements for its low-carbon economic operation. Research has made some progress in system design and planning, simulation modeling and optimized dispatch, but less attention has been paid to environmental benefits from the perspective of openness. According to the practice of existing new energy micro-grid projects, this paper studies the correlation between market transaction and regional energy system , and introduces a method for constructing a carbon-neutrality management information system under linkage mechanism of multi-markets. This paper also gives the illustration for analyzing implementation process of low-carbon route under specific situations, which shows the green, low-carbon, collaborative and open characteristics of the carbon-neutralization management information system.
To alleviate the problems of high operating cost of PV-integrated EV charging station and large load fluctuation on the grid side, an optimization algorithm combining NSGA-III (nondominated sorting genetic algorithm III) and fuzzy clustering is proposed for optimization operation of energy storage. Firstly, on the basis of analyzing the structure of the charging station, a multi-objective operation model of the energy storage is established to minimize the load variance of grid side, the operation and maintenance cost of the energy storage, and the purchase cost from the grid. Then, NSGA-III algorithm is applied to solve multi-objective model. For the Pareto sets containing a lot of information, it is difficult for the operators to make a decision. A method based on fuzzy clustering is proposed to select the optimal solution from Pareto sets. Finally, extensive experimental analyses demonstrate the efficiency of the proposed method, and show that, compared with particle swarm optimization, the economy of the system and the grid-side load level are improved on the basis of the load demand, so that the overall performance of the charging station is optimal comprehensively.
Integration of renewable energy, electric vehicles (EV), and inverter air conditioners (IAC) in the business park microgrid (BPMG) result in fluctuation and uncertainty, which lead to new challenges in operational flexibility of the BPMG. Therefore, a BPMG optimal scheduling method considering demand-side flexible resources is presented. Moreover, the flexible supply provided by BPMG demand-side flexible resource is quantified according to the demand response models of EV and IAC. And then, combined with the flexible resource and flexible demand at the source side, flexibility shortage severity is proposed for quantifying the imbalance severity between the flexible supply and flexible demand. On this basis, a BPMG multi-objective optimal scheduling model considering economy and flexibility is constructed, which can be solved by CPLEX optimization software. Experimental results show that the proposed model can significantly improve the operational flexibility of BPMG by coordinating various flexible resources on both supply and demand sides.
Photovoltaic intelligent edge terminal (PVIET) is one of the important equipment for safe and stable operation and intelligent operation of PV power station. It is powerful but expensive, and the distributed PV power station with small capacity cannot afford its high price. In order to solve the above problems, an optimal configuration method for PVIET is proposed, and the optimal layout model of PVIET is established from two aspects, i.e., quantity and location. At the same time, aiming at the weak optimization performance and low operability of coyote optimization algorithm (COA), a mutation opposition-based learning COA (MOBL-COA) is proposed. The test results of typical cases show that the proposed method can effectively reduce the cost, and the proposed MOBL-COA has excellent performance in convergence speed, solution accuracy and algorithm stability, which verifies the effectiveness and feasibility of the proposed method.
When an outage event occurs, coordinating local distributed generators to restore critical loads is an effective way to enhance the resilience of the power distribution system (PDS). PDS and water distribution system (WDS) are two kinds of lifeline infrastructures with interdependency in a city. If we establish the restoration strategy without enough consideration of the interdependency between the two systems, the lifeline infrastructures may not work properly. This paper analyzes the interdependency between PDS and WDS firstly. On this basis, authors formulate the distribution system restoration method considering the interdependency between PDS and WDS as a mixed-integer linear program (MILP) through several linear approximation methods. The objective function is maximizing the number of restored critical loads, and the operational characteristic of PDS, WDS, and the interdependent components are taken into account. The method is tested in an integrated power and water system. The result shows that the proposed method can restore more critical loads and make the allocation of limited generation resources optimally.
When converters are connected to grids, the negative-sequence control (NSC) may deteriorate the system’s stability since it changes the typical PLL-based vector control, especially in weak grids. This paper analyzes this issue. Firstly, the proportional-integrational quasi-resonant (PIR) control, as one of the widely-used NSC methods, is introduced. Then, we derive the amplitude-phase impedance model and primal-dual complex circuit of the converter-connected grid for the case that the grid voltage is balanced, in which the converter applies the PIR control. Then, we analyze the impacts of the change caused by the PIR control on the admittance elements of the converter’s amplitude-phase impedance. Besides, the generalized impedance criterion is applied to analyze the impact of NSC on the system’s stability. The analysis results show that the NSC may cause the sub-synchronous oscillation issue and nearly 100 Hz oscillation issue. The simulation results verify the analysis results.
In view of the contradiction between the distribution accuracy of distributed generation (DG) output powers and high voltage level in the traditional droop control of DC microgrid, a DC-microgrid droop control based on double voltage compensation is proposed in this paper. The method is based on the centralized secondary control and the peer-to-peer droop control strategy, and double voltage loop control is designed into the secondary control. The first voltage loop ensures high accuracy distribution of load power, and the second voltage loop raises the system voltage closer to the rated value. The strategy makes DC microgrid system have good steady state and dynamic characteristics, can realize the accurate distribution of output power among converters with different capacity, and can deal with local load mismatch. The simulation results show that the proposed control method can ensure good power distribution accuracy, maintain the voltage at a high level, and take into account the dynamic response of the system.
During the frequency decline process of a power system subject to a disturbance, unnecessary under-frequency load shedding will be triggered if the instantaneous frequency is too low. This will reduce the power system reliability. Energy storage can be used to mitigate the unnecessary transient under-frequency load shedding due to its fast power regulation ability. According to the equivalent frequency response model of power systems, this paper proposes a multi-objective dynamic optimization model with piecewise function constraints which is suitable for the configuration of energy storage capacity. This model aims to consider two conflict objectives at the same time, such as the cost of energy storage configuration and the performance of transient frequency regulation. The big-M method and implicit trapezoid integration method are used to transform the multi-objective dynamic optimization model into a multi-objective mixed integer quadratic programming model. Then the normalized normal constraint method and CPLEX solver are used to obtain the Pareto optimal solution. Finally, the effectiveness of the proposed multi-objective optimization model of energy storage capacity configuration is validated with the simulation result on the IEEE 24-bus system.
Information security assurance is essential for the safe and stable operation of power cyber-physical systems. The key is to conduct all-round real-time monitoring of power cyber-physical systems and analyze the collected massive monitoring data to make accurate security risk assessments. As an evolutionary algorithm for pattern classification, the gene expression programming (GEP) has received widespread attention due to its ability to perform global search, but its operation on high-dimensional data sets is extremely time-consuming. This paper proposes an enhanced GEP algorithm for power grid information security risk assessment. The algorithm first uses the idea of rough set, solves the optimal attribute through the discrimination function to reduce the data sample, and then uses the niche genetic algorithm improves the diversity of the reduced sample individuals to accelerate the convergence speed of the GEP algorithm, and then realizes the global search through the genetic algorithm and obtains the level assessment of the security risk. Simulation results show that, compared with traditional security risk assessment algorithms, the enhanced GEP algorithm proposed in this paper has a higher attribute reduction rate and global convergence rate, and can quickly implement risk assessment from massive monitoring data of grid security information.
Non-intrusive load disaggregation can accurately portray the user’s power consumption portrait by recovering the information of single electrical equipment on the power consumption side from the total electric meter information, which plays an important role in the refined management of the consumers. Aiming at the problems of low decomposition accuracy and low training efficiency of current artificial neural network models in load decomposition, this paper studies and builds a non-intrusive load disaggregation model based on temporal convolutional neural network. By analyzing the power consumption of the device, the dilated causal convolution is applied to perform convolution operations on the power sequence of electric meter and to expand the receptive field and extract richer features. The network training efficiency is improved by adding residual connections, weight normalization layers and optimizing training data window. Finally, the constructed model is tested on the optimized UKdale data set. The experimental results show that mean absolute error, root mean square error, and relative error are all in a relatively small range, and the time complexity analysis further shows that the model has a shorter training time without losing the load decomposition accuracy.
Small hydropower stations could contribute to the development and utilization of intermittent renewable energy (IRE) power generation such as wind power and solar power, as a significant flexible resource in a power system. Given this background, hydropower pricing and energy management strategies for promoting accommodation of large amount IRE power generation are addressed. First, small hydropower stations are divided into two classes according to their daily regulation capacity, namely coordinately-scheduled hydropower stations (CHS) and independently-scheduled hydropower stations (IHS), and a framework of hydropower pricing and energy management with multiple entries considered is proposed on the basis of a Stackelberg game. Then, a bi-level optimization model is established, where the peak-valley electricity prices for the IHS as well as the power output of the CHS are determined at the upper level, with an objective of minimizing the curtailment of IRE power generation and hydropower. Each IHS determines its own power output at the lower level, with an objective of maximizing its generation profit, and a non-linear bi-level optimization problem is attained. The optimization problem is transformed into a mixed integer liner programming problem by applying the Karush-Kuhn-Tucker (KKT) optimality condition and the strong duality theory, which can be solved by the Yalmip/Gurobi solver. Finally, case studies based on an IEEE sample system are carried out and simulation results demonstrate that the developed strategies could promote the accommodation of IRE generation.
As the global trend to low-carbon power system transition, under the current power market framework, whether the market mechanisms can release correct price signals to ensure the adequacy of flexible power supply and reliable backup capacity in power system, whether the capacity remuneration mechanism (CRM, or CM) is necessary, and how to design the CRM are important issues for maintaining the sustainability of the power market. This paper firstly discusses the possible generation adequacy problems that may arise when a single energy market model is adopted for a high proportion of new energy power system, and quantifies the problems by constructing a screening curve model for power generation technology. Secondly, this paper summarizes the mechanism and specific contents of capacity remuneration mechanism design in foreign mature power market, and extracts three key elements of capacity remuneration mechanism design. Lastly, this paper points out the problems of capacity market mechanism design in China’s power market and provides forward relevant suggestions.
The use of comprehensive evaluation for scientific and quantitative analysis of the growing new energy big data service (NEBDS) platform plays an important role in promoting and improving the construction of data service operation. At first, considering the purpose and demand of NEBDS, the comprehensive index system for evaluating the operational efficiency of NEBDS is constructed from the four aspects, i.e., economy, technology, environmental benefits and social services. At the same time, the combination of subjective and objective weight method based on improved analytic hierarchy process and entropy is also studied by application of the principle of minimum discriminatory information. Secondly, in view of the fact that decision-makers have different subjective tendencies in value judgment when facing gains and losses, starting from assessing both the limited rationality and risk avoidance of investors, the comprehensive evaluation method for ranking the optimal new energy big data service projects is proposed by using the prospect theory to improve the TOPSIS method. Finally, the effectiveness of the proposed comprehensive benefit evaluation method is verified by simulation.
