Under the deepening of China’s power market reform, the deregulation of the power system is resulting in increasing transmission congestion and corresponding congestion revenue. Allocating congestion revenue effectively and reasonably is critical for achieving stable operation of the power system and ensuring orderly competition in the electricity market. According to the experience of foreign developed power market, this paper firstly summarizes the existing common problem of domestic congestion revenue allocation such as merely using the unified settlement price of users as the reference settlement price of medium and long term contract, which is followed by an analysis of the actual practice commenced in Guangdong using the numerical example to further demonstrate the inner evolution logic, while providing relevant suggestions for future development. The paper suggests that the medium and long term contract settlement reference price should be decided by both contract parties on their own, and restriction on user’s settlement price should be lifted so that the nodal price can effectively send market signal.
Renewables have the “positive externalities” of clean and low-carbon. Meanwhile, they also have the “negative externalities” of uncertainties and fluctuations. To handle the aforementioned uncertainties and fluctuations, the current electricity spot market mainly adjusts the market clearing boundary according to the operating experiences, which cannot explicitly reflect the changes in the power balance characteristics caused by the integration of renewables. As a result, the regulation function of the electricity spot market on stimulating the flexibility of power system operation cannot be effectively performed. To solve this, the impact of uncertainties and fluctuations of renewables on multiple market clearing boundaries and cost characteristics is analyzed. The idea of characterizing the aforementioned impact in the market clearing model is explored. Then, taking the frequency-regulation mileage service as an example, the impact of renewables on the frequency-regulation mileage requirement is revealed. On the basis of these, the intra-day market clearing model considering the response mode of generators to the frequency-regulation mileage requirement is proposed, which can explicitly reflect the aforementioned impact. Simulation results demonstrate that the proposed method can improve the system frequency performance while guaranteeing the system economy performance, which validates the effectiveness of the proposed method.
In the operation of the electricity spot market, there are generally many forms of market power on the power generation side, great influence, difficult prevention, and difficulty in supervision, which bring huge risks and challenges to the operation of the electricity market. Aiming at the design goals of incentive compatibility, individual rationality and social welfare maximization, this paper proposes a market power risk prevention method that embeds market risk prevention measures into the spot market clearing algorithm. The market main body of the same-day market trading behavior and historical behavior performance is comprehensively considered. Through the establishment of a violation penalty cost and a systematic price right auction settlement mechanism in the market clearing link, the priority of clearing market players for violations is reduced, and the intention of market players to exercise market power is effectively restrained, so that risk prevention will be put before risk prevention. It is placed in the clearing link to prevent risks before they occur, and effectively solve the problems of difficult disposal and impact recovery after market power risks occur. Finally, the IEEE 30-node system example is used to verify the feasibility and effectiveness of the method.
With the construction of the power market, the proportion of electricity transaction through the market is growing significantly. Meanwhile, the grid is gradually changing from the planning mode dominated by conventional units to the market mode with high proportion of renewable energy. Hence, the power system operation is facing serious challenges due to the marketization of the main behavior of power generation as well as the tendency of the system to be extremely complicated. Under the background of high penetration rate of new energy in power system, this paper discusses the absorption problem of renewable energy in the electric power market, and tries to put forward a solution to promote the efficient absorption of wind and solar power. Firstly, on the basis of the simulation of high-penetration-rate new energy power system, the simulation of new energy and acquire the typical output relying on the scene reduction algorithm are carried out. Later, a day-ahead clearing model considering real-time market and the network constraints is proposed, at the same time, the penalty of wind abandoning and load loss are included. Finally, quantitative comparative analysis are made for different scenarios of wind abandoning and load loss, which reveals a feasible consumption model after high proportion of renewable energy is connected to the power system. Such framework will help realize the double carbon target from the perspective of electricity market.
Under the guidance of the double carbon goal, the total installed capacity of new energy in China has been increasing year by year, and the problem of new energy consumption has become prominent. With the increasing reform of the electricity market, small and medium loads such as electric heating can participate in electricity market transactions through aggregation, promote source-load interaction, and promote new energy consumption. In this paper, in the day-ahead market, wind farms are used as power generators, and electric heating load aggregators are used as demand side. According to the length of thermal inertia time, the segmented bidding curves of are provided. The sorting method is used to conduct bilateral transactions of electricity prices in the trading center. The improved Shapley value method is used to comprehensively consider the user’s economy and comfort, and reasonably allocate the transaction electricity on the demand side according to the personal satisfaction. Finally, the effectiveness of the method proposed in this paper is verified by an example. The results show that the method proposed in this paper can coordinate the benefits of source and load to maximize social welfare; at the same time, it can improve the overall satisfaction of the society by taking into account the user’s comfort and economy.
Under the background of “double carbon target ”, the new power system carries the important task of low-carbon transformation of power system. In order to better promote the absorption of new energy and effectively promote the demand response to participate in the operation of power system, this paper proposes a multi-objective optimization model of active distribution network considering gravity energy storage and demand response. Objective function 1 considers the total cost of active distribution network operation and user energy consumption, and objective function 2 is selected as the equivalent carbon-emission cost of active distribution network. Due to the uncertainty of wind power output, the uncertain optimization model is converted to the deterministic optimization model by using the robust optimization theory, and the operation optimization solution method based on NSGA-Ⅱ algorithm is constructed. Finally, the model is solved in three scenarios. The results show that the system equipped with gravity energy storage and the demand response at the same time can not only reduce carbon emissions, but also achieve the purpose of peak-cutting and valley-filling, and the system has better operation economy.
In order to achieve frequency stability of interconnected multi-area power system, load frequency control needs to obtain signals and send commands through open communication network. In the open communication network, the system will inevitably be affected by the interference of external environment such as weather and network attacks, which will cause the frequency fluctuation of power grid. These network attacks such as Denial-of-Service (DoS) attacks will block a certain amount of signal transmission, thus reduce the performance of the load frequency control scheme, and even lead to the instability of the power system. This paper investigates a resilient load frequency control (LFC) for multi-area interconnected power system with measurement channel and control channel under DoS attacks. Firstly, the hypothesis of DoS attack frequency and duration is given, then a flexible elastic controller is designed to ensure the stability of the input state of the closed-loop system, and an appropriate sampling mechanism is determined to achieve an effective compromise balance between system performance and communication resources. Finally, the effectiveness of the proposed method is verified by the simulation of a two-area interconnection power system.
With the large-scale access of distributed power generation, the traditional fault-recovery strategy for distribution network is difficult to be applied to large-scale blackouts caused by extreme disasters. Firstly, this paper proposes a strategic framework to optimize and improve the resilience of distribution network considering the coordination of source, network, load and storage under extreme disasters. Secondly, aiming at the uncontrollability and time variability of distributed energy output, the PV-storage and wind-storage (PWS) system models are established. At the same time, considering the relationship between electricity price demand response (DR) and load demand, a load demand response (LDR) model under extreme disasters is established. Thirdly, aiming at maximizing the total value of load recovery, considering the cost of network loss in the process of LDR compensation, fault repair and network reconstruction, a resilience promotion model for distribution network considering the source-network-load-storage collaborative optimization is established. Finally, the effectiveness of the proposed method is verified in an improved PG &E 69-node distribution network system. The results show that the source-network-load-storage cooperative optimization according to the characteristics of multi-energy complementation is beneficial to improve the fault recovery ability of the distribution network.
To cope with the fast frequency-stability evaluation problem with a few sparse samples, this paper proposes an intelligent method for power system dynamic frequency-response curve prediction based on extreme gradient boosting (XGBoost). This method adopts serial integration of multiple regression trees to finely mine the non-linear mapping relationship between input features and dynamic frequency response curve; sensitive factors are introduced into the loss function to reduce the impact of differences in sample distribution during the training process, and particle swarm optimization (PSO) is used to automatically tune the hyperparameters in the XGBoost. This method can not only fast produce maximum rate-of-change of frequency, frequency nadir, quasi-steady state frequency, but also predict the inertia-center frequency response curve. Case study is conducted based on a provincial power system in China, and results are compared with those obtained by time-domain simulation, shallow neural network and deep learning methods to verify the advantages of the proposed method.
In case of distribution network single-phase grounding fault, characteristics are difficult to capture, which may affect the selection correctness and thus bringing hidden dangers to the safe and stable operation of the power grid. Hence, a new method based on attention mechanism and convolutional neural network (CNN) is proposed. Firstly, zero-sequence current data is preprocessed by S-transform. Secondly, Attention-CNN model is established by introducing attention mechanism to CNN. Finally, performance of the proposed model is verified by both simulation and real grid data, and compared with other methods under different fault conditions. Results show that the proposed Attention-CNN model can accomplish more efficient and accurate selection, as well as wide application.
Field programmable gate array (FPGA) has the advantages of high data parallelism and high operating frequency, which can meet the needs of micro-second step precision simulation of power system, and shows great potential in the real-time simulation of the microgrid. For the further study of dynamic characteristics of photovoltaic (PV) and energy storage system (ESS) in the microgrid, the real-time simulation system of independent PV-ESS low-voltage DC microgrid is designed based on FPGA. Firstly, the real-time simulation framework of the DC microgrid is built, and a series of typical modules of the electrical system is designed. Secondly, considering the different dynamic requirements of real-time simulation, the PV-ESS control system model covering a variety of control strategies is built. Then, a hierarchical coordinated control strategy based on DC bus Signaling (DBS) is proposed, which balances the energy flow of the system by coordinating the control strategy of the PV-ESS converter. Finally, the FPGA real-time simulation results are compared with Simulink software. On the one hand, the accuracy and reliability of the FPGA real-time simulator are verified. On the other hand, it is also verified that the hierarchical coordination control strategy can smoothly switch the working layer, balance the power flow of the system, and effectively maintain the voltage stability of the DC bus.
The AC/DC hybrid power distribution system based on solid-state transformer (SST) is of great significance for large-scale consumption of renewable energy. Reliable control strategy is the key to ensure stable operation of hybrid power distribution system. In this paper, the coordinated operation control strategy of AC/DC hybrid distribution system based on SST is proposed. The control strategy of “source, storage and load” in the subnet can realize the power balance in the subnet independently. The low-voltage level can maintain the low-voltage AC bus voltage and realize the mutual assistance of AC and DC subnets. The isolation level coupling medium-voltage DC bus and l low-voltage DC bus, support the medium-voltage DC bus voltage. Medium-voltage level can construct medium-voltage AC bus voltage, realize mutual support between low-voltage and medium-voltage levels, and adopt voltage-source output form when off-grid and grid-connected, without switching control strategy, and cooperate with pre-synchronization control to realize seamless switching between modes. RTDS simulation results show that the system can adopt a unified control strategy, and achieve power coordination and seamless switching between multiple modes in the AC/DC hybrid distribution system based on SST without communication, which verifies the effectiveness and feasibility of the proposed control strategy.
The short-circuit ratio has a big impact on the small-signal stability of the wind power grid-connection systems. Conventional mechanism analysis or simulation mainly focuses on the impact of parameters on the stability under specific operating conditions rather than all possible operating conditions. To fill this gap, this paper proposes a method based on multiple linear regressions. In this paper, the mechanism and analysis methods of the two concerned types of small-disturbance stability (including synchronous stability and sub/super-synchronous oscillation stability) are firstly introduced. Secondly, the definition of short-circuit ratio in the single-infeed wind power systems is reviewed. Then, a statistical analysis method based on the multiple linear regressions is constructed to assess the impact factors by numerous stability analysis cases. Finally, the method is applied to the grid-connected PMSG and DFIG systems to quantify the impact of the short-circuit ratio on the small-disturbance stability. The results can be used to guide the application of the short-circuit ratio in the small-disturbance stability evaluation of the wind power systems.
With the characteristics of easy deployment, self-organization and sustainability, the rechargeable wireless sensor network (RWSN) can control the depth and breadth of information sensing in all aspects of the microgrid flexibly, enhance the precise control and intelligent dispatch of the microgrid remote monitoring system. The complex working environment of islanded microgrids increases the difficulty of accessing stable power sources from the main network as well as the operation and maintenance of mobile charging devices. This paper selects high-density and easy-to-deploy solar energy as the main energy source of the network, and builds a distributed solar-powered self-sustainable WSN framework for smart monitoring of isolated microgrids, which investigates the energy scheduling with Simultaneous Wireless Information and Energy Transfer (SWIPT) for solar-powered cluster heads (SC), where the SC coordinates SWIPT to periodically broadcast energy to a set of wireless charging nodes in full-duplex mode, each member node exhausts the recharged energy for data sensing and forwarding. To maximize system energy efficiency, this paper proposes a solar harvesting collaborative SWIPT Energy Scheduling optimization problem, which jointly optimizes the deployment of SCs, the energy broadcast power with time allocation, and data sensing size under the constraint of the self-sustaining demand for network energy. Simulation results show that the proposed algorithm has improved the energy efficiency of the network and the stability of the PV energy supply network over existing algorithms, and meets the demand for full-time monitoring of the information collection system, providing a green, efficient and sustainable un-manned information collection system for the energy management of isolated microgrids.
Blockchain technology has been applied to all walks of life since it was proposed. Because of its transparent and secure transaction environment and jointly maintained node information, it has been applied to virtual power plants (VPP) to meet the needs of VPP aggregation of various distributions. The flexible application of the energy supply system with clean and low-carbon energy as the main body, as well as the safe operation and comprehensive defense system of the power system. Starting from the concepts of blockchain and VPP, this paper analyzes the applicability and scenarios of VPP in four aspects: consensus mechanism, encryption technology, distributed storage and smart contracts, and finally analyzes the application of blockchain in VPP applications. The problems encountered are classified and summarized, aiming to improve the flexibility of VPP and distributed power transactions and the portability of data management, and provide ideas for further combining VPP and blockchain to optimize the structure of the power market.
