Abstract:With the large scale operation of electric buses (EBs), the arrangement of their charging optimization will have a significant impact on the operation and dispatch of EBs as well as the charging costs of EB companies. Thus, an accurate grasp of how external factors, such as the weather and policy, affect the electric consumption is of great importance. Especially in recent years, haze is becoming increasingly serious in some areas, which has a prominent impact on driving conditions and resident travel modes. Firstly, the grey relational analysis (GRA) method is used to analyze the various external factors that affect the power consumption of EBs, then a characteristic library of EBs concerning similar days is established. Then, the wavelet neural network (WNN) is used to train the power consumption factors together with power consumption data in the feature library, to establish the power consumption prediction model with multiple factors. In addition, the optimal charging model of EBs is put forward, and the reasonable charging time for the EB is used to achieve the minimum operating cost of the EB company. Finally, taking the electricity consumption data of EBs in Baoding and the data of relevant factors as an example, the power consumption prediction model and the charging optimization model of the EB are verified, which provides an important reference for the optimal charging of the EB, the trip arrangement of the EB, and the maximum profit of the electric public buses.

As renewable energies become the main direction of global energy development in the future, Virtual Power Plant (VPP) becomes a regional multi-energy aggregation model for large-scale integration of distributed generation into the power grid. It also provides an important way for distributed energy resources (DER) to participate in electricity market transactions. Firstly, the basic concept of VPP is outlined, and various uncertainties within VPP are modeled. Secondly, using multi-agent technology and Stackelberg dynamic game theory, a double-layer nested dynamic game bidding model including VPP and its internal DERs is designed. The lower layer is a bidding game for VPP internal market including DER. VPP is the leader and each DER is a subagent that acts as a follower to maximize its profit. Each subagent uses the particle swarm algorithm (PSA) to determine the optimal offer coefficient, and VPP carries out internal market clearing with the minimum variance of unit profit according to the quoting results. Then, the subagents renew the game to update the bidding strategy based on the outcomes of the external and internal markets. The upper layer is the external market bidding game. The trading center (TC) is the leader and VPP is the agent and the follower. The game is played with the goal of maximum self-interest. The agent uses genetic algorithms to determine the optimal bid strategy, and the TC carries out market clearance with the goal of maximizing social benefits according to the quotation results. Each agent renews the game to update the bidding strategy based on the clearing result and the reporting of the subagents. The dynamic game is repeated until the optimal equilibrium solution is obtained. Finally, the effectiveness of the model is verified by taking the IEEE30-bus system as an example.

The openness of the electricity retail market results in the power retailers facing fierce competition in the market. This article aims to analyze the electricity purchase optimization decision-making of each power retailer with the background of the big data era. First, in order to guide the power retailer to make a purchase of electricity, this paper considers the users' historical electricity consumption data and a comprehensive consideration of multiple factors, then uses the wavelet neural network (WNN) model based on "meteorological similarity day (MSD)" to forecast the user load demand. Second, in order to guide the quotation of the power retailer, this paper considers the multiple factors affecting the electricity price to cluster the sample set, and establishes a Genetic algorithm-back propagation (GA-BP) neural network model based on fuzzy clustering (FC) to predict the short-term market clearing price (MCP). Thirdly, based on Sealed-bid Auction (SA) in game theory, a Bayesian Game Model (BGM) of the power retailer's bidding strategy is constructed, and the optimal bidding strategy is obtained by obtaining the Bayesian Nash Equilibrium (BNE) under different probability distributions. Finally, a practical example is proposed to prove that the model and method can provide an effective reference for the decision-making optimization of the sales company.In 2014 alone, the power trade with EEX reached an astonishing 1952 tWh. The Pennsylvania New Jersey Maryland (PJM) exchange in the USA is currently responsible for the operation and management of power systems in 13 U.S. states and the District of Columbia. As a regional Independent system operation (ISO), PJM is responsible for centrally dispatching the largest and most complex power control area in the USA and ranks third in the world in scale. The PJM controlled area accounts for 8.7% of the total population (about 23 million), load 7.5%, installed capacity of 8% (about 58,698 MW), and transmission lines up to more than 12,800 kilometers [4]. The effective application of power big data for the profitability and control of power companies has a high value. Some experts have said that whenever the data utilization increases by 10%, it can cause the power grid to increase profits by 20% to 49%. In the face of a huge power system, data needs to be processed quickly, hence data mining technology came into being, and will play a key role in supporting the sales company to participate in market competition.Power-selling is the pillar business of the retailer. As an intermediate link between production and use, the power retailer needs to analyze the users' needs and predict the user's load demand based on the users' historical electricity consumption data, considering industry, meteorology, regional economy, related industries, and other factors, so that this can guide the retailer to develop electricity purchase. In recent years, numerous scholars have put forward many effective load forecasting methods. Vilar, J. et al. provided two procedures to obtain predi...

No abstract

In active distribution system (ADS), the access of distributed generation (DG) can effectively improve the power supply capacity (PSC). In order to explore the effect of DG on the PSC, the influence of accessed DG on the power supply of ADS has been studied based on generalized sensitivity analysis (SA). On the basis of deriving and obtaining the sensitivity of the evaluation indexes of the PSC to the parameters of connected DG, seeking for the DG access instruction for the purpose of improving the PSC, PSC evaluation model with inserted DG is established based on SA. The change degrees and trends of the PSC and its evaluation indexes caused by the slight increase of DG are calculated rapidly, which provides reference for the planning and operation of ADS. Finally, the feasibility and validity of the proposed theory are validated via IEEE 14-node case study.