Under the traditional trading model of power generation rights, the transaction price needs to meet certain conditions to drive the self-contained power plant to participate in new energy consumption, which cannot fully release the peak-shaving potential of the captive power plant. Moreover, under the centralized transaction system, there are problems such as high operation and maintenance costs and low transaction efficiency. The energy access service model of self-contained power plants is proposed to expand the space for new energy consumption and adopt a decentralized transaction mechanism to improve transaction efficiency. At the same time, a decentralized transaction matching model is constructed. With the goal of maximizing the consumption of new energy and minimizing the cost of energy access services, it comprehensively considers the constraints of power plant interests, unit power space, consumption space and grid security, and builds an optimization model for power generation rights and energy access power. Analysis of calculation examples shows that by providing energy access services, self-contained power plant can effectively increase the consumption of new energy, improve the overall economic benefits of the market, and provide a useful reference for improving the mechanism of new energy consumption.
With the promotion of integrated energy system, the coupling of multiple energies in terms of supply, transmission and use increases, the forms of energy use are gradually diversified, and the connotation of retail market is constantly enriched under the reform of energy market. Under the traditional retail market of single energy, independent supply and pricing method cannot effectively guide users to use energy and improve the economic efficiency. Therefore, it is urgent to study the pricing strategy for retail market of integrated energy system. In this paper, optimal retail pricing method for the comprehensive energy park on the basis of value at risk (VaR) theory is proposed considering multi-energy load, randomness of spot price and demand response. A two-stage model of static retail pricing and day-ahead optimal scheduling is established to study the optimal static retail pricing strategy of integrated energy service providers. The first stage is a retail pricing optimization model that takes into account day-ahead energy load and spot price randomness. The second stage is the day-ahead optimal scheduling model under the optimal retail price in the first stage. The particle swarm optimization (PSO) linear programming algorithm is used to solve the two-stage model iteratively. The results show that VaR-based retail pricing can effectively reduce the risk of integrated energy service providers participating in the retail market.
The ability of single microgrid to deal with the uncertainty of the output of various distributed power sources is limited. Power transaction among multiple microgrids in an area is one of the effective methods to improve the consumption of local renewable energy. However, the traditional centralized transaction platform has the problems of high platform maintenance cost, untimely transfer of funds and opaque transaction information. In this situation, applying the distributed data storage and peer-to-peer transaction technology of blockchain, this paper designs smart contracts for multi-microgrid market transactions to help microgrids make decisions. Under the energy coordination architecture of multi-microgrid trading market, the microgrid firstly adopts adjustable robust optimization to formulate a scheduling plan that considers the uncertainty of renewable energy output. Secondly, the judgmental smart contract is used to judge the market’s trading mode. Then, smart meters are used to make adaptive price and the transaction matching is completed by the distributed transaction smart contract. Finally, the transaction plan determined by the distributed transaction smart contract can be used as the input data of adjustable robust optimization of the single microgrid to explore feasible solutions. At last, a better economic solution is selected as the optimal day-ahead output plan and trading plan for the microgrid under the worst distributed power output scenario. Simulation results indicate the effectiveness of the presented method.
The vigorous development of renewable energy is of great significance for optimizing China’s energy structure and promoting the sustainable development of energy. In order to promote the consumption of renewable energy, it is an effective way to allow it to participate in the electricity market. However, the development of renewable energy relies on an effective incentive mechanism. The existing incentive policies for renewable energy require government control and cannot be applied for a long time when market reforms are gradually deepening. In response to the above problems, this paper constructs a decentralized power market structure suitable for renewable energy, proposes a fully market-oriented renewable energy incentive mechanism, and analyzes the decision-making behavior of power producers. The simulation results show that the proposed market structure and incentive mechanism can effectively promote the participation of renewable energy in the market clearing.
With the rapid development of clean and renewable energy industry, the existing energy structure is difficult to meet the needs of energy production and market. An energy industry innovation is imperative. As a promising development direction of the next generation energy infrastructure in academic and industrial circles, energy internet has been widely concerned. Its basic characteristics of openness, interconnection, equivalence and sharing provide a rich vision for future energy development. However, the existing matured information technology solutions cannot fully satisfy the requirement of energy internet from design idea to project implementation. As a rapid development technology, blockchain has the characteristics of distribution, equality, security and traceability, which is highly consistent with the design idea of energy internet, and is expected to become the key technology for the implementation of energy internet. Energy blockchain is the product of the combination of blockchain and energy industry, which can provide security and value support for all levels of energy internet. By positioning the key technologies of power trading blockchain in the energy internet, this paper summarizes the research progress of the current power trading blockchain in the aspects of consensus mechanism, transaction and smart contract design, security mechanism and other fields of technology. Combined with the research status, this paper discusses and analyzes the problems existing in various technical fields and the possible future, and provides a reference for further research and implementation of energy blockchain.
At present, the penetration rate, charging frequency and charging capacity of electric buses are relatively high, so the charging load has a non-negligible impact on the operation and dispatch of the power grid. So, the charging load forecasting research has important theoretical and practical significance, but the intermittent and random charging behavior increase the spatial forecasting difficulty. Therefore, the charging load forecasting method of electric buses is proposed on the basis of spectral clustering and long short-term memory (LSTM) neural network. First of all, the charging load curve is clustered according to spectral clustering considering the distance and the shape. And then, considering the key factors that affect the charging load, such as historical load, temperature and day type, the model parameter of LSTM neural network is trained using each cluster charging load, and the charging load of each cluster is predicted. Then, the total charging load of the forecasting day is to sum the forecasting results of different clusters. Finally, on the basis of the historical real data in a certain city, the proposed method is verified. The result shows the mean absolute percentage error (MAPE) of charging load prediction result of the proposed method is below 11%, and the accuracy of load forecasting is improved.
Study of the charging load characteristics of electric vehicles (EVs) is the basis for promoting the integration of EVs into the power grid. The charging load of electric vehicles has both temporal and spatial randomness. In this paper, a temporal and spatial distribution model of EVs, which considers the characteristics of different urban functional areas, is proposed on the basis of the improved gravity model. Firstly, the travel characteristics and the categories of EVs are determined, and urban areas are reasonably divided according to the categories of functional areas. Secondly, the travel matrices of urban areas at different time period, as well as the spatial distribution of EVs, are achieved according to the improved gravity model. Finally, combining with the charging modes and the state-of-charge (SOC) model of EVs, the temporal and spatial distribution model is established. Case studies show that there exists a huge discrepancy in the load distributions among different types of functional areas due to the attractiveness of each area. For the same type of functional area, areas closer to the city center generally pick up more load than their outlying counterparts. The accuracy of the proposed model is verified by analyzing the influence of different factors on the charging load distribution in various preset scenarios.
With the rapid growth of electric vehicles (EVs), the charging characteristics of large-scale EVs are randomness and spatiotemporal coupling, which poses a risk of exceeding the limit on the operating voltage of distribution network. Through the demand response (DR) based on price, it has become an important technical means to guide the orderly and reasonable charging of EVs in a large space-time range. In this paper, the DR characteristics of EV charging station based on data-driven and its participation in the operation optimization of distribution network are studied. Firstly, the charging model of single EV and the driving characteristics of EV considering the topological structure of traffic network are proposed, and the load simulation calculation method of regional EV charging station is established. On this basis, the electric power system based on LSTM deep neural network is proposed. The mapping model between charging cost and power response of EV charging station is obtained by encapsulating the DR model of EV charging station. Furthermore, a voltage operation optimization model of regional distribution network considering the DR of EV charging station is constructed, and the model is solved with particle swarm optimization algorithm. Finally, the comparison and analysis of the 33-node system with 3 charging stations verify the effectiveness of the proposed method of EV charging station DR and its participation in distribution network operation optimization. It provides reference for data-driven method to solve the problem of EV charging and demand response.
With the popularity of electric vehicles (EVs), a large number of disorderly charging behaviors have a negative impact on the reliability of the distribution network. In this paper, a reliability evaluation model of distribution network considering demand response and road-electricity coupling characteristics is established to accurately predict and dispatch the spatiotemporal distribution load of electric vehicles, so as to improve the reliability index. Firstly, the road-electricity coupling model structure and the time-space load forecasting framework are proposed. Then the road network model, user model, and charging load supplement model considering demand response are given, and the space-time distribution of electric vehicle load is obtained. Finally, evaluation based on the two-way hierarchical structure and heuristic reduction strategy is carried out for the reliability of the distribution network where the EVs connect. Taking a regional road-electricity coupling network as an example, the temporal and spatial distribution of private EV charging load in this area is analyzed and the reliability of the distribution network considering demand response and road-electricity coupling characteristics is evaluated. Simulation results in various scenarios verify the effectiveness of the proposed model, the optimization effect of demand response on reliability index, and the deterioration effect of road-electricity coupling characteristics on the reliability index.
In view of the exclusive characteristics of distributed sources and loads in the island microgrid, an optimal energy dispatching method for island microgrid is proposed in this paper to achieve economic operation while considering renewable power accommodation. Firstly, according to performance analysis of typical ocean power generation, the optimization model of energy dispatching for island microgrid is constructed involving wind power, photovoltaic power, diesel engine generating sets, energy storage devices, wave power, tidal power, and controllable loads. Then, on the two timescales of day-ahead and intra-day, microgrid power dispatch for the day could achieve the global optimum by using mixed integer programming on CPLEX, in which the day-ahead schedule is revised continuously in accordance with the intra-day scrollable calculation results by comparing with the intra-day real-time scrollable deductive results. The analysis shows that, by taking advantage of day-ahead and intra-day combined scrollable calculation, the prediction accuracy of the sources and loads on the island can be improved step by step, and the proposed dispatch optimization method can achieve power balance and higher renewable energy utilization while minimize the operation and maintenance costs of island microgrid.
In the face of the energy crisis and environmental pollution problems around the world, wind power generation is one of the effective solutions. Compared with onshore wind power, offshore wind power has the advantages of rich wind resources, no land restriction and high annual utilization hours, so it has broad development prospects. Collection systems are important parts of offshore wind farm, which affect planning investment and reliable operation of the whole wind farm. Firstly, topologies of the AC and DC collection systems and use of circuit breakers are compared and analyzed. Secondly, optimal design of collection systems is systematically explained from the perspectives of economy and reliability. Then, key equipment affecting collection systems development and technical state are summarized. Finally, challenges and future prospect of collection systems are presented.
In order to solve the voltage violation problem in a distribution network that integrates multiple distributed generators, this paper proposes a method based on distributed model predictive control for coordination of PVs and energy storage systems and for dynamic voltage regulation in a distribution network containing distributed energy storage systems. Firstly, we analysis dynamic characteristics of distributed PVs, energy storage systems and voltage sensitivity model of the distribution network, and construct the whole distribution network model considering dynamic characteristics. Considering large scale and numerous devices of distribution network, we divide it into several subsystems through ε decomposition. Then, this paper designs distributed model predictive controller containing various limits of PVs and energy storage systems and transfers this into solving a QCQP problem to optimize control instructions, and then restoring from voltage violation fast in distribution network. Finally, the simulation result in IEEE 33-node network verifies the validity of proposed method.
With the continuous expansion of the power market, distributed generation (DG) operators enter the market as new stakeholders. Large amounts of DG operators configuring the power in active distribution network will become the future trend. It is an important issue for DG operators to operate well in the power market and ensure their economic benefits when configuring DG. In view of this problem, this paper analyzes the cooperation relationship between the DG operators and the distribution company, considers active management fee in the comprehensive profit of the DG operator, takes the active management strategies into account implemented by the distribution company during the operation, and builds a bi-level optimal configuration model of DG. This paper takes the DG operator as the upper body and optimizes the DG configuration with the goal of maximizing the comprehensive profit, and takes the distribution company as the lower body and optimizes the operation with the minimum DG removal amount as the goal. This paper establishes a joint probability scenario to account for the uncertainty of DG and load. An improved harmony search algorithm and an interior point algorithm based on the filtering set are used to solve the model. The analysis results of the example show that the bi-level optimal configuration model of DG conforms to the current state of the power market. The consideration of active management strategies and active management fee achieves mutual benefit and win-win for DG operators and distribution companies, and the model provides a reference for the sustainable development of DG operators.
With the development of integrated energy system (IES), electric demand response has received widespread concentration as a method for releasing the potential of distribution network (DN). This paper proposes an EDR mechanism under IES. Firstly, a transaction framework is proposed for three stakeholders in the EDR: grid operator (GO), electric response coordinator (ERC), and integrated energy user (IEU). Then, in order to maximize the reliability improvement benefit of the DN, maximize the economic benefit for ERC, maximize IEU satisfaction and minimize the cost of energy consumption, a tri-stakeholder transaction model based on game theory is founded. Next, a benefit sharing means of equaling economic benefit from reliability improvement between GO and ERCs, and a cost allocation of equaling economic cost from electricity consumption decline between ERC and IEUs is established, which is caused by GO and IEUs participation in EDR. Finally, a method of optimization and solution for the proposed model based on the ACT theory and NSGA algorithm is presented, and case simulation demonstrates the validity and reasonableness of the proposed model.
In order to further promote the construction of electricity spot market in China, it is the future development trend of the power spot market to operate on the market-oriented trading mode, in which the power generators and loads participate together and compete fairly. Base on the fact that the research on the demand response (DR) of spot market is still in its infancy in China, and how to participate in the market for DR is an urgent problem to be solved. This paper proposes a new market trading mechanism with clearing mode that takes into account the incentive demand response (IDR). Firstly, an intermediate agent approach has been introduced to integrate IDR resources as one to participate in the wholesale market for dealing with the distributed characteristics. Next, an adaptive IDR trading mechanism has been discussed with the development of electricity spot market in China, and an effective benefit evaluation method for clear spot market has been analyzed for IDR. After that, the bidding model of IDR in the wholesale market is constructed according with the providers’ bids, and the day-ahead and real-time market clearing model with IDR is designed in the joint optimization mode. Finally, the effectiveness of the proposed method and model is verified by the quantitative analysis of the market benefit of IRD through the IEEE 30-node system.
