A data center represents an important flexible resource on the user side. Through reasonable scheduling and motivating demand response, the capability of the data center to accommodate the power generation from distributed renewable energy sources (RESs) can be effectively improved, which is conducive for achieving the specified “dual carbon” target in China. In this paper, the optimal dispatch and demand response strategies for a data center are addressed to promote the accommodation of the power generation from RESs. Firstly, considering the load characteristics of the data center, a conventional data center operation model including the power demand profile and quality of service (QoS) is established. Then, according to the schedulable characteristics of delay-tolerant workloads, a renewable energy subsidy mechanism is proposed for the data center to motivate the shift of the user data service demand to the peak power generation period of RESs. Then, on the basis of the green certificate (GC) mechanism, with the objective of minimizing the operating cost of the data center and maximizing environmental benefits, an optimal dispatch model of the data center is presented. Finally, a typical data center is employed to demonstrate the feasibility and efficiency of the presented method. Simulation results show that the presented method can effectively explore the demand response potential of the data center and its users, enhance the accommodation capability for power generation from RESs, and hence reduce carbon emission.
In the context of carbon neutrality, fast response natural gas power generators and new energy storage systems, such as power-to-gas (P2G) and batteries, will play a crucial role in the power system. However, due to the high fuel cost of natural gas power generators, they will face financial risks in the electricity market. The purpose of this paper is to design an approach to apply the financial options, especially short put options, long put options and short call options, energy storage devices involving P2G and battery to earn extra monetary benefits during the idle hours of a gas-fired power generators in an electricity market and to also hedge the risk of electricity price fluctuations. For short put with the energy storage devices, it has considered the P2G and the battery. To determine the optimal weight of the three types of options to invest, they will be arranged into a portfolio by using the mean-variance theory. Case studies results verify that the risk management strategy proposed in this paper can help natural gas power generators play a better role in the low-carbon transformation of the power system.
In order to achieve low-carbon energy in the power system, the measures like vigorously developing carbon capture and storage technologies and new energy sources such as wind and solar power are very important. However, the technical constraints of the minimum output of carbon capture power plants and the reverse peak-shaving characteristics of wind power have restricted the wind power consumption and carbon emission reduction. The liquid storage tank under the flexible operation mode of carbon capture is used to carry out “energy time-shift” and “carbon transfer”, which can indirectly absorb wind power and reduce carbon emissions. CO2 captured by carbon capture equipment may be used as the raw material for power-to-gas to reduce carbon storage costs and power-to-gas costs, which is helpful to furtherion eliminat of wind power and obtain gas sales revenue. Because the load has morning and evening peaks, the good peak-regulation performance of the thermal storage type concentrating solar power (CSP) station is used to transfer part of the heat during the day to the evening peak power generation, so as to relieve the system’s peak-regulation pressure. To this end, this paper constructs an optimal scheduling model for virtual power plants containing power-to-gas and solar thermal power plants considering the comprehensive and flexible operation of carbon capture power plants. The commercial solver Cplex in the YALMIP toolkit of Matlab software is used to optimize the model in this paper. The simulation results show that the proposed model can further reduce wind curtailment, reduce carbon emissions and alleviate peak-shaving pressure.
Focused on carbon capture, utilization and storage (CCUS) technology, development of relevant policies from major developed economies in recent years are introduced. In the respective of national and local governments, relevant policies or plans for CCUS in China are reviewed and analyzed comprehensively. In the past decade, the release of these policies or plans had strongly promoted the layout and development of CCUS technology in China, and it has come into the stage of industrial demonstration. However, compared with developed economies such as the United States and the European Union, China still has a certain gap on the level of technology development and project scale, and there exist some deficiencies mainly including lack of finance and tax incentive or subsidy policies, slow construction for system of laws, regulations and standards, incomplete layout of CCUS industrial chain, and relatively weak speaking right in international cooperation, etc. In the future, it is suggested that according to the basic national conditions, on the basis of learning from the good policies and practical experience from other countries, China should speed up the research and development of low-cost, low-energy-consumption, safe and reliable CCUS technologies and the construction of industrial clusters, and deepen international cooperation. Then the leading advantage of CCUS industry development could be established gradually which is beneficial to achieve the double carbon target.
Offshore wind power integration system applying hybrid cascaded DC transmission has the advantages of high voltage, large capacity and low investment cost, but its stability has gained many concerns. And the system impedance modeling is the basis for analyzing stability. In this paper, a detailed mathematical model of VSC-DRU hybrid cascaded valve station is established. In order to realize active power distribution and AC voltage control of the DC transmission system, the power-voltage control strategy of VSC with filter capacitor current feedback is studied. Considering the influence of output phase-angle adjustment of phase-locked loop, active damping control and power control, the detailed impedance model of VSC-DRU hybrid cascaded valve station is derived and analyzed by using small-signal disturbance method, which provides a basis for studying stability. Finally, the simulation results in PSCAD/EMTDC show that the proposed control has good performance of power-voltage regulation. And the correctness of the established detailed impedance model is verified by the frequency-scanning method.
ing at the problems existing in the control of modular multilevel converters (MMC) on both sides of DC-DC converter in the intermediate isolation stage of three-stage power electronic transformer (PET), a control method of model predictive control (MPC) based on independent value function is proposed, which establishes independent value functions for multiple control objectives without configuring the weight of value function in MPC. The method solves the difficult problem of weight allocation of value function in traditional MPC, and reduces the amount of calculation of MPC controller. In addition, aiming at the problems of low reliability, poor flexibility and small adaptability of the existing three-stage DC-DC converter in the intermediate isolation stage of PET, the topology of DC-DC converter based on MMC in the intermediate isolation stage of PET is designed in this paper. The design improves the flexibility of power supply and realizes the uninterrupted power supply ability of PET by adopting the structural design of double high-frequency transformer. Finally, the system model platform is built, and the correctness and effectiveness of the proposed control strategy are verified by experiments.
The large-scale grid-connection of new energy sources will bring new impact on the dynamic safety and stability of the power system, so the safe, stable and efficient utilization of new energy sources is one of the future directions of energy development. Different types of new energy sources have different effects on the transient stability of power systems, and this problem can be better solved by using the “region” analysis method. This paper proposes to construct and analyze the dynamic safety region applying the improved dynamic differential evolution-interior point method (DDE-IPM), which has the advantages of high speed, low error and high accuracy compared with the traditional method. The time-domain simulation in DIgSILENT/PowerFactory calibrates the dynamic safety region of the system under different types of new energy and different penetration rate under fault, and analyzes the low voltage ride-through capability of the system under different penetration rates, and draws the following conclusions: the dynamic safety region hyperplane of the system is parallel in nature and the boundary distance is proportional to the capacity of the new energy under each penetration rate of the system. The compensation equipment of the new energy source supports the transient safety of the system within a certain range, and the dynamic safety region of the system is expanded; both wind power and PV plants of the system have the LVRT capability of grid-connected operation.
Perceiving state and identity of real-time electrical load is the premise of refined energy management and demand response. To meet the automatic and precise control requirements of smart electric appliance network, the on-line identification technology of appliance-level load state and type is proposed. On the basis of real-time measurement and communication of multi-appliances, the state of load is extracted by improved CUSUM segmentation and hidden Markov model, and the single classification method SVDD is used to identify the electrical appliance type extensively. The accuracy, timeliness and extensive ability of the proposed methods are verified by constructing smart electric appliance network in residential and office buildings.
With the development of demand response (DR) business and interactive regulation of “source-network-load-storage”, as more and more demand response terminals access the power network, it is necessary to carry out the prediction and defense technology research on the distributed denial of service (DDoS) behavior of demand response terminals. Aiming at the current network attack research of power system, this paper focuses on the self-similar characteristics of attack traffic, and proposes a network attack model based on ensemble empirical mode decomposition (EEMD) and long short-term memory (LSTM). The detection method firstly extracts the modal features by ensemble empirical mode decomposition attack traffic, then detects the attack applying the improved LSTM neural network, and finally carries out the simulation experiment and comparative analysis. Compared with the traditional LSTM detection method, the EEMD-LSTM neural network detection method has better dynamic performance and effectively improves the DDoS attack detection accuracy.
The load frequency control is of vital importance to maintain the stable operation of the island microgrid. Aiming at the frequency control problem when the microgrid is subjected to strong random disturbances and network topology parameters change, this paper proposes a load frequency control strategy based on deep Q-learning (DQN) for island microgrid with electric vehicles. Firstly, a frequency control model of electric vehicle considering the randomness of user charging behavior is established, and a load frequency control (LFC) model for microgrid including photovoltaic power, wind power, micro gas turbines, electric vehicles and their random power increment constraints is built. Secondly, the structure of frequency controller based on DQN is designed, and the definitions of state space, action space and reward function are completed in turn, and the optimal hyperparameters is obtained through adjustment. Finally, the simulation results show that, compared with PI control and FUZZY control, the DQN controller proposed in this paper has the ability of online learning and experience playback, which can deal with the LFC problem of microgrid with strong randomness more effectively, and it can better adapt to the complex operating conditions of system parameters and structure changes.
As an important part of achieving the goal of “Carbon Neutrality”, the low-carbon transformation of the power system has always been a key issue for China’s energy industry. In order to better realize the optimal allocation of energy resources and the smooth realization of low carbon targets, this paper established a planning model of China’s future power structure applying multi-stage stochastic programming and multi-region power structure planning methodology. The model is objected to minimize the total expected cost during the planning period, and subjected to constraints including load balancing, inter-regional transmission and peak regulation. The proposed model also introduces the non-anticipated constraint which is necessary for the sequential decision making of multi-stage planning, thus achieving the optimal schemes in sequential and better simulating the decision making process. Considering the uncertainty of the future load and electricity consumption growth, this paper optimizes the power capacity structure and power generation structure in China from 2020 to 2060, and forecasts the future power sector development in China.
With the development of the energy market, many emerging market agents participate in competition. The increasingly complex coupling of interests between the operators and users of the regional integrated energy system (RIES) make it difficult to formulate effective collaborative optimization strategies. Therefore, this paper proposes an operator-user collaborative optimization strategy based on the distributionally robust game model for RIES. Regarding RIES operators as leaders and users as followers, a Stackelberg game model accounting the uncertainty of wind power on the energy supply side is established. In this model, the leader aims to maximize revenue and establishes a data-driven distributionally robust decision model of the combined heat and power RIES, which takes into account the uncertainty of wind power. Followers establish a comprehensive benefit optimization that considering energy substitution behavior according to time-of-use energy prices. In order to simplify the solution of the proposed distributionally robust game model, the follower model is equivalent to an equilibrium constraint and added to the leader’s decision-making model applying KKT condition, and the two-level game model is transformed into a single-level sub-Brue-bar optimization model. Then, the McCormick method is used to linearize the bilinear term in the leader’s objective function. Finally, the problem is solved by the C&CG algorithm. The experimental results show that, the strategy can satisfy the interests of users and guide them to use energy reasonably through energy price difference, and effectively deal with the uncertain risk of wind power output.
In view of the impact of distributed photovoltaic and wind power grid-connection on power grid dispatching and operation, a multi-energy virtual power plant (VPP) including photovoltaic, solar thermal power, wind power and energy storage batteries is constructed by combining energy storage batteries with the flexibility of concentrating solar power plant (CSP). According to the characteristics of CSP and energy storage battery, the operation strategy is formulated according to time-of-use (TOU) electricity price, and a two-stage optimal dispatching model of virtual power plant is established with the goal of maximizing the net income in each period. In the day-ahead scheduling, it optimizes the CSP output and formulates the declaration and output plan by considering the TOU electricity price and sunlight factors. In the real-time scheduling, the deviation of wind power and photovoltaic output is corrected by using the charge and discharge of thermal storage system, CSP is assisted by energy storage battery to adjust the deviation, and the adaptive particle swarm optimization algorithm is used to optimize the output of each unit. Finally, the rationality of the model and the feasibility of the operation strategy are verified by simulation. The results show that the combined regulation of CSP and energy storage battery can effectively reduce the deviation of real-time output tracking declared output of virtual power plant and improve economic benefits.
As the forefront of “new infrastructure construction” and “grid digitization”, the “multi-station integration” makes full use of integrated resources to build a variety of new infrastructures. The “multi-station integration” can not only respond to the “Internet Plus” strategy and building China’s strength in digital, but also open up new forms of energy sharing and mutual benefit. This paper focuses on the multi-station integration project in the digital transition of the power grid. And this paper constructs a system dynamics model for economic-environmental-social benefit analysis in multiple business scenarios. This paper simulates the trend of economic, environmental and social benefits under different scenarios. The results show that: 1) In term of economic benefits, different businesses can achieve expected economic benefits under certain conditions; 2) Environmental benefits are the “Induced Effect” of energy operations in multi-station integration. The multi-station integration projects can help improve the efficiency of waste gas emission reduction; 3) Social benefits are mainly embodied in the conservation of three social capital: land resources, energy resources and human resources. It is confirmed that the economic-environmental-social benefit analysis model is scientific and effective.
Improving energy utilization efficiency is one of the main goals of the construction of integrated energy service stations. In order to reflect the characteristics of energy utilization, conversion and loss of integrated energy service stations, this paper firstly considers three aspects of macro-energy efficiency micro-energy efficiency and energy economy. Six evaluation indices for energy utilization, renewable energy utilization, EV charging station energy efficiency, data center energy efficiency, energy economic cost, and economic development adaptability are proposed, and an energy efficiency evaluation index system for integrated energy service stations is established. Secondly, the weighted directed graph is used to simulate the internal energy flow of the integrated energy service station, and the calculation method of the system energy flow and the energy efficiency evaluation method are proposed. Finally, this paper takes an integrated energy service station as an example, an evaluation scenario of integrated energy service station is established, and the proposed method is applied to conduct a comprehensive energy efficiency evaluation of the system. The results show that the model and evaluation method proposed in this paper can reflect the energy utilization in the system, is suitable for the pre-evaluation of system energy efficiency, and has reference significance for the planning and operation of the integrated energy service station.
