The main problem that affects the accurate prediction is non-stationarity of wind power and photovoltaic power. The signal decomposition technique can decompose the non-stationary power sequence into several intrinsic mode component of different frequencies, smoothing the fluctuation degree of the sequence, extracting sequence features, establishing a prediction model with strong adaptability, and improving the prediction accuracy. This paper firstly classifies and divides the signal decomposition technology, and systematically reviews the application research status of domestic and foreign researchers on the signal decomposition technology from the perspectives of time domain decomposition and frequency domain decomposition. Secondly, the application research examples of the signal decomposition technology are reviewed in detail from the two aspects of wind power and photovoltaic power prediction, and the advantages and disadvantages of the decomposition technology are compared through the actual decomposition results. Finally, the application scenarios of the signal decomposition technology are summarized, and the application fields of the decomposition technology and the research direction of improving the prediction accuracy are prospected.

Accurate prediction of wind power is of great significance to promote large-scale integration of wind power for the grid. Most of the existing researches focus on single-step prediction in the ultra-short-term range. In order to achieve multi-step prediction of wind power which is closer to the engineering application, this paper proposes a multi-step prediction method of wind power based on ensemble empirical mode decomposition and encoder-decoder. First, the k-means cluster algorithm is employed to group wind turbines, and then the ensemble empirical mode decomposition algorithm is used to decompose the power sequence of each group of units. In this way, the temporal and spatial distribution characteristics of wind power are extracted. Advanced multi-step prediction of the wind power is achieved through the encoder-decoder prediction network based on the gated recurrent unit. Finally, by reconstructing the prediction values of all subsequences the total power in a wind farm will be obtained. Taking the data of a real wind farm as example, the simulation results show that the performance of the proposed algorithm is better than other traditional models in different application scenarios such as 1~6 hours ahead, and the prediction accuracy is improved by 6.45%~13.56%.

In the context of energy structure transformation, the penetration of distributed energy resources in power systems is gradually increasing, and the distribution network is no longer a simple passive network, resulting in closer interaction between transmission and distribution networks. The limitations of the traditional transmission and distribution split energy management mode are prominent. Firstly, this paper summarizes the relevant research results of transmission and distribution co-optimization under vertical monopoly management system from four aspects. Then, aiming at three scenarios including distribution companies participating in the centralized market and distributed energy resource aggregators participating in the centralized market, and distributed energy resources participating in the local market during electricity market reform process, the relevant research results of transmission and distribution co-optimization under market environment are sorted out. Finally, the transmission and distribution co-optimization of power systems with distributed energy resources is summarized and prospected.

The construction of HVAC/HVDC transmission channels and the development of the inter-provincial and inter-regional power market are advancing steadily in China. However, there is no research on the optimal power distribution calculation of the HVAC/HVDC transmission channels under the power market environment. A two-stage HVAC/HVDC transmission channel power optimal distribution method under the medium and long-term power market environment is proposed. In the first stage, according to the protocol electricity, the cooperative calculation between the optimal scheduling of intra-region generation and transmission and the optimal power distribution of HVAC/HVDC transmission channels is realized by using the alternating direction method of multipliers. In the second stage, according to the market increment electricity, the transmission channel power distribution scheme adjustment model is established. The final HVAC/HVDC transmission channel power distribution scheme is obtained through two-stage distribution calculation. Simulation results of a three-region HVAC/HVDC hybrid system verify that the proposed method can achieve the reasonable power distribution of transmission channels under power market environment.

In order to quantify the contribution of energy storage equipment to system carbon emission reduction and its impact on system flexible peak shaving, a bi-level optimization model of energy storage considering system carbon emission and system regulation risk is established. Firstly, the output carbon emission model of thermal power units is improved and the carbon emission generated by thermal power units is described in detail, then a carbon emission measurement model considering the charge and discharge power of energy storage is proposed. The upper model determines the charging and discharging power of energy storage at each time to minimize system carbon emission, load peak valley difference and energy storage operation cost. Then, a wind power operation risk cost function with adjustable energy storage capacity is established. The lower model aims at minimizing the risk cost of load shedding, wind abandonment and power regulation of thermal power units, and determines the energy storage reserve capacity at each time under the constraint of the energy storage charge and discharge power obtained from the upper layer. In terms of algorithm, immune genetic algorithm is used to solve the upper problem, and the CPLEX solver in MATLAB and point estimation method are used to jointly solve the lower problem. The simulation results show that the model can reduce the peak valley difference of load and reduce the system carbon emission, so as to quantify the contribution of energy storage to emission reduction. By setting the reserve capacity of energy storage, the peak shaving flexibility of the system is improved and the operation risk of the system is reduced. The upper energy storage power optimization model and the lower energy storage reserve capacity optimization model constrain each other, so as to provide reference for low-carbon and flexible operation of the system.

The “dual carbon” goal accelerates the rapid development of new power system with new energy as the main part. It is urgent to realize the cooperative control of distributed multi-region power grids in new power system. From the perspective of automatic generation control (AGC), a cooperative control reinforcement learning algorithm for power grid with new energy is proposed to obtain the cooperative control of distributed multi-regional power grid. The proposed algorithm solves the problem that the action values are overestimated or underestimated in the classical Markov Q learning and its derivative algorithms through the weighted thought, and uses the delayed update strategy to further accelerate the convergence speed. The improved two-area load frequency control model incorporating electric vehicles and the distributed five-region interconnected grid model with new energy are simulated to verify the effectiveness of the proposed algorithm. Compared with the other existing control algorithms, the proposed algorithm has better control performance and faster convergence speed.

As the proportion of wind power in the power system increases, the uncertainty of wind power output makes the frequency stability problem of high-proportion wind power power systems more and more serious. Traditional units need to reserve frequency regulation reserve capacity to cope with the frequency fluctuation caused by load and wind power uncertainty. Resulting in severe wind curtailment during the load valley period. The variable speed wind turbine can effectively relieve the frequency regulation pressure of traditional units by participating in the frequency regulation of the power grid through the operation mode of load shedding. To this end, based on the over-speed control and pitch angle control of variable speed wind turbines, a control strategy for load-shedding operation of wind turbines under full wind conditions is proposed. Secondly, the optimal dispatch model of the wind-thermal-storage joint system in which the wind power-energy storage participates in frequency regulation is constructed, and the system frequency regulation reserve constraints are set based on the opportunity constraints, considering the operation mode of wind power dynamic load shedding participating in frequency regulation, the load shedding rate of wind power is optimized. Finally, it is verified by an example that the method proposed can effectively promote the consumption of wind power and improve the economy of system operation.

With the increasing proportion of installation capacity of new energy, photovoltaic’s participation in grid frequency regulation has become a research hotspot. However, the existing research alaways aims at frequency regulation effect, and directly gives parameters such as the droop coefficient based on experience, it lacks practical reference value for engineering. This paper studies the problem from two dimensions: frequency regulation strategy and techology. Taking two frequency regulation technologies of load shedding operation and additional energy storage as the main line of the text, and two frequency regulation strategies of droop control and virtual inertia control as the research objects, a universal method for designing frequency regulation parameters is proposed based on the power frequency static characteristics and rotor motion characteristics of traditional units, and the essence of the two types of frequency regulation technologies is revealed. Taking the power grid of a new energy-rich area in Guizhou as the example, the regional power grid model is built in the MATLAB/Simulink simulation platform, frequency exceed/not exceed the PV frequency regulation limit as the simulation conditions are designed and compared with previous work. The results verify the accuracy and effectiveness of the proposed parametric design methods. At the same time, comparing the frequency modulation effects of the two types of technologies, it is found that in the same scene, the additional energy storage technology can achieve better frequency modulation effects.

The construction of virtual power plant （VPP） provides an effective way to exploit the flexibility of power grids in mega-cities, but the existing researches and practices are still unable to support the organic coordination between VPPs and regional unified power markets under the dual-carbon goals. This paper comprehensively summarizes the current status of the VPP constructions and market mechanisms, and deeply analyzes the technical challenges of VPPs in mega-cities participating in the regional power market. Based on the construction needs of regional power market and future power system, the research directions of key supporting technologies such as secure grid connection, operation control, low-carbon dispatching, support systems, etc., for VPPs are further proposed. In addition, the future development prospects are prospected. It is expected this work can provide references to promote the secure, orderly and sustainable development for regional power markets, power grids and VPPs, and promote the construction of new power systems in mega-city power grids.

In view of the phenomenon of low market enthusiasm caused by the traditional power grid electricity price monopoly model, this paper proposes an energy trading model of one leader with multi-followers based on dynamic pricing. The Stackelberg game Nash equilibrium solution between distribution network operators and virtual power plants is obtained by setting up multiple scenarios to analyze the energy trading volume between them in integrated energy system. During the game, the distribution network operator, as the leader, is responsible for summarizing the transaction electricity of the virtual power plant. Then the distribution network operator decides the transaction price by considering the price response behavior of the virtual power plant to maximize the economic benefits. Virtual power plants as followers, in order to minimize operating costs as the goal, according to the transaction price to determine the transaction electricity. At the same time, a solution method combining Kriging meta-model and genetic optimization algorithm is proposed to solve the equilibrium solution of both sides while simplifying the calculation. Finally, an example is given to prove that the proposed game model and optimization algorithm can not only improve the solving efficiency, but also improve the economic benefits of both parties.

Virtual power plant （VPP） aggregates distributed power and load-side adjustable resources participating in energy market trading, which has become an effective way to absorb new energy and tap the demand response potential. Under this background, a VPP dispatching model based on multi-objective and two-stage programming is constructed. In the day-ahead stage, the maximization of market operation income and user energy absorption satisfaction has been taken as the goal to construct the multi-objective optimization model of VPP. The Pareto optimal solution of the multi-objective problem is solved by NSGA-‍Ⅱ algorithm, and the comprehensive benefit index is proposed to select the best solution. In the real-time stage, the minimization of deviation penalty cost and wind abandonment cost has been taken as the goal to construct the robust programming model of VPP, considering the influence of the flexible thermoelectric ratio of combined heat and power （CHP） plant on the wind power absorption. Finally, an actual VPP is analyzed to verified the multi-objective two-stage programming model. The results show that the proposed model can improve its energy utilization efficiency on the premise of ensuring the comprehensive benefits of VPP.

In recent years, virtual power plant （VPP) has been developing rapidly as an effective means of aggregate utilization of renewable energy. With the expansion of VPP grid-connected scale, the problem of multiple interconnected VPP transactions has become increasingly prominent. Aiming at the transaction game between multiple VPPs, considering the characteristics of the physical network, cooperative game strategy of multiple VPPs considering the operational constraints of distribution network is proposed in this paper. Firstly, considering the operation constraints of the distribution network, the internal resources of the VPP are integrated and modeled, and then the VPP energy management model is established. Secondly, through the introduction of peer-to-peer （P2P) energy transactions, the autonomous energy management and collaborative pricing of multiple VPPs systems can be realized, and P2P energy transactions can be achieved without harming the interests of all parties. At the same time, considering the uncertainty of electricity load and renewable energy output, a typical scenario generation algorithm is used to construct a fuzzy set of probability distribution of uncertain variables. Aiming at the privacy problem arising from multi-agent transactions, column constraint generation algorithm combined with alternating direction multiplier method are adopted to solve the model in this paper. Finally, a numerical example is simulated on the IEEE 123 nodes test system, and the simulation results verify the effectiveness of the proposed model and algorithm.

With the proposal of the dual carbon goal of “carbon peak and carbon neutralization”, a large number of photovoltaic with strong randomness will be connected to the distribution network. It has become a new challenge to improve the accommodation capacity of photovoltaic and reduce the abandonment rate of photovoltaic. In addition, the energy storage system plays an important role in stabilizing the random fluctuation of photovoltaic. Considering the uncertainty and temporal correlation of photovoltaic output, this paper proposes a distributionally robust optimal allocation method of photovoltaic and storage collaborative allocation aiming at photovoltaic effective accommodation capacity, and takes the location and capacity of photovoltaic station and energy storage device as decision variables at the same time. Firstly, the reference joint probability distribution of photovoltaic output considering time correlation is statistically driven by historical data, the ambiguous set is constructed based on Jensen-Shannon divergence distance, and the model is transformed into a mixed integer convex programming model by using second-order cone technology to accelerate the solution, and then the joint configuration scheme of optical storage is solved by column and constraint generation algorithm. Finally, an actual 180 nodes distribution network is taken as an example to verify the effectiveness of the proposed method.

Due to the inherent intermittence of the high proportion of renewable energy, the problem of consumption has become increasingly prominent. Electric to hydrogen and electric vehicles are important technologies for achieving energy conservation and emission reduction, and participate in grid optimization and dispatch as flexible resources, which are beneficial for the consumption of renewable energy. In this paper, a day-ahead optimal dispatch model of wind power distribution network with P2H and EVs in consideration of source-load coordination is proposed. Firstly, a model based on the energy storage and adjustable characteristics of electric to hydrogen and electric vehicles is established to realize the improvement of load characteristics. Secondly, combining the demand for wind power consumption and peak shaving and valley filling in the system, a dynamic time-sharing cost model based on power value and power change rate is proposed, and a flexible resource control strategy of source-load coordination is formed. Finally, a day-ahead dispatch model of distribution network with wind power is established, which aims at economic optimization. The simulation experiment in the modified IEEE 33-bus distribution network system shows that the proposed dispatch method can effectively reduce the total system scheduling cost and load peak-valley difference, and address wind power consumption.

In recent years, more and more distributed flexible resources have been integrated in the smart grid, such as electric vehicles and renewable energy, which promotes the rapid transformation from the passive distribution networks to active distribution networks. However, this process also brings great challenges to the operation and optimization of the power system. This paper investigates the energy management framework in the active distribution networks of multiple distributed energy resource clusters. The framework aims to minimize the total cost, and also optimizes the power loss in the process of peer-to-peer energy transactions. In order to ensure the reliable and safe operation of distribution networks, relevant network constraints are incorporated. The proposed framework adopts Nash bargaining theory, which can obtain a mutually beneficial and fair solution, and encourage clusters to participate in energy transactions. Finally, several case studies applied to an improved IEEE 33-bus distribution network show that the proposed framework improves the flexibility, economy, and security of energy transactions, and has effectiveness and superiority to some extent.

Under the policy background of “double carbon”, fully exploring the potential of low-grade heat source and improving the level of clean energy supply in the park is the focus of current research. This paper considers low-grade heat source endowment and load demand in the park, puts forward a kind of park level distributed clean energy supply strategy which means a distributed energy hub station is established by adopting the heating and power supply mode of “low-grade heat source with electric heat pump and waste heat generation equipment”. Taking the superior energy network as energy supply supplement, energy hub station flexibly adjusts the system energy supply parameters according to the real-time load demand of users. In order to realize the above energy supply strategy, firstly, a multi-objective optimization method of park-level energy supply cluster division is proposed by comprehensively considering the endowment of energy and load resources, geographical location and matching relationship. Secondly, the electric-heating cooperative planning model considering the constraints of electric and heating power flow and the output of electric-heating coupling equipment is proposed to optimize the equipment capacity allocation of energy hub stations. Finally, the piecewise linearization method is used to simplify the model. The results of an energy supply demonstration park to be planned show that the proposed strategy achieves efficient utilization of local low-grade heat source, and it has strong coupling relationship between electricity and thermal energy flow. Compared with the conventional electric-heating cooperative energy supply method, and the primary energy consumption and carbon emission level of the park are reduced.