Aiming at the lack of coordination and optimization of multiple flexible loads under combined heat and power operation and the uncertainties faced by wind, solar power, and flexible loads, a robust optimization scheduling method for virtual power plant(VPP)combined heat and power considering flexible loads is proposed. Fully considering the coordination and complementarity among the reduction, translation and transferable flexible loads, the standard deviations of the thermal and electric load curves and the lowest comprehensive operating cost of the system are chosen to be the goals, and a dual-layer coordinated optimization operation model of virtual power plant combined heat and power is established, which may improve the coordination and optimization effect of multiple flexible loads. The prediction error uncertainty of wind and solar power and flexible load is dealt with by the robust optimization method to improve the system operation robustness. The coordination and optimization of multiple flexible loads enhance the system's flexible adjustment capabilities and make the economy better. The comparison of the optimization effects of flexible loads under different robustness coefficients provides a reference for decision makers in different demand scenarios. The analysis of an example verifies the effectiveness of the proposed model.
Micro energy grid is the development direction of the terminal energy supply of the energy Internet (EI). The economic optimization of the system urgently needs to tap the potential of multi-energy loads, and a single demand response cannot meet the coordinated optimization of “source-network-load”. In this paper, aiming at the coupling conversion characteristics of multiple energy sources in the micro energy grid, a generalized energy hub model including energy storage and integrated demand response (IDR) is established. Energy operators of micro energy grid are introduced and three comprehensive demand response models, namely, transfer model, transforming model and reduction model are established. A day-ahead optimization scheduling method for micro energy grid considering comprehensive demand response is proposed, in which time-of-use energy prices are formulated, three demand response modes are coordinated and optimized. A mixed integer linear programming model with the goal of profit maximization is established, and the solution is solved by CPLEX software. Combined with a calculation example, the effectiveness of the comprehensive demand response model and the day-ahead optimal dispatching method proposed in this paper is verified. Results show that the multi-energy load coupling and complementary capabilities of the micro energy grid are maximized, and the economic efficiency of system operation is improved.
In order to solve the problems of inefficient energy utilization and high energy cost in the production process of protected agricultural industrial park, a multi-objective optimization scheduling method for integrated energy system (IES) of protected agricultural industrial park is proposed, which takes into account the local consumption of wind and solar energy. Firstly, the output model of integrated energy system of protected agricultural industrial park including renewable energy such as wind, solar and biogas power is established. Then, considering various constraints, taking the minimum energy consumption and maximum consumption rate of wind and solar energy as the objectives, the improved multi-objective sine-cosine algorithm based on reverse learning is used to solve the problem. Finally, the integrated energy system of a protected agricultural industrial park is taken as an example. The results show that the optimal scheduling method can effectively reduce the daily operation cost of the park and improve the local consumption rate of wind and solar energy.
Aiming at the contradiction between multi-subject benefits and system optimal scheduling under multi-microgrid joint economic dispatch,this paper proposes a multi-microgrid joint economic dispatch model considering dynamic AC power flow constraints for distributed optimization and emerging benefit redistribution. The alternating direction multiplier method (ADMM) is used to convert the global variables such as tie-line power and the power traded between microgrid and the main network into local variables. The overall system optimization goal is achieved on the basis of satisfying the local optimization conditions. And Shapley value method is used to redistribute the emerging benefits according to the marginal contribution of each micro-network to the system,to solve the conflict between system optimization and individual interest. The simulation results of the IEEE 33-node system show that the proposed model can effectively overcome the defects of independent and centralized optimization, significantly improve the regional system economy and local wind and solar power consumption,reduce the communication burden and protect the operation privacy of the microgrid, effectively solve the problem of unfair distribution of emerging benefits among alliance members at the same time.
With the rapid development of EVs, more and more attention has been paid on the dispatching of electric vehicles participating in micro-grid. Studying the energy management of EV users' requirements is helpful to the stable and economic operation of micro-grid. An economic dispatch strategy of DC micro-grid, which takes into account the needs of EV users, is proposed in this paper. When considering the different demands of EV users for charging time and charging speed, an EV classification model under the graded charging price is established, including four charging types such as instant type, elastic type, compensating type and night type. Users participating in micro-grid dispatching upload reservation information through mobile App one day in advance. The micro-grid dispatch center integrates the user appointment information and the day-ahead forecast data of renewable energy and important loads, and takes the goal of minimizing the operating cost of the system and the converted cost of the power fluctuation of the tie line, considering the relevant constraints of each unit, so as to realize the reasonable planning of the charging time of EVs. Finally, an example is given to verify the economy and effectiveness of the scheduling strategy.
The security of the DC distribution system requires the system to maintain the ability to supply power in response to a set of expected faults. N-1 check, which reflects the strength of system security, is used as a criterion for judging whether the system can continue to carry load under the condition of system fault. At present, no corresponding evaluation standard are studied on the security of DC distribution system. In order to study the transfer mechanism of fault sysem, open-loop topology is analyzed to establish a simplified security region model. Firstly, the parameters required by N-1 check are defined, and mathematical relationship between these parameters is derived, which is to reveal the network architecture of the system with fewer parameters. Secondly, the transfer mechanism of feederN-1 fault and converter station N-1 fault is analyzed. According to the defined parameters, the transfer process is described, and the N-1 constraint conditions of the feeders and converter stations are obtained. Therefore, the security region model is established. Thirdly, applying the security region model, the total supply capability of the system is calculated. Also, the relationship between power supply capability and load balance, which can be drawn as CS-s2 curve, is deduced. Thereafter, the optimal solution is found by using the entropy order method to analyze the curve. Finally, the feasibility of the model proposed in this paper is verified by comparing with existing security region model in the example.
In order to support the exploitation and power delivery of the large-scale wind and solar power bases located in the Eastern and Northern Areas, a lot more west to east and north to south ultra-high voltage direct current (UHVDC) power transmission projects will be built. Interconnection with UHVDC network will impact the power flow, dynamic stability and short-circuit current level of the metropolitan 500 kV power grid. A systematic methodology to evaluate the impact of interconnection with UHVDC on metropolitan 500 kV network is proposed. To handle the problem of short-circuit current over-limitation, two measures are proposed to change the grid structure and the line parameters, respectively. The simulation results validate the effect of both the measures in limiting short-circuit current caused by UHVDC interconnection.
In the era of energy internet, distribution networks face the demand for the storage and computing of massive distributed data. Edge computing can reduce the computing and storage pressure of master stations by utilizing terminal resources of distribution networks to provide nearby analysis and computing. In order to give full play to the advantages of edge computing and improve the level of refined operation and management of distribution networks, this paper proposes a method for edge division of distribution network, which is based on Infomap algorithm. Firstly, considering the scale of edges and the demand for autonomy, the definition of edge range and principles of edge division in distribution networks are clearly given. Then, theN-1 security check model of a distribution network, which is based on the path description, is proposed, where the switching times of tie-switches measure the tightness of connection between edges. Finally, on the basis of community detection of complex networks, a distribution network is abstracted into a graph model, and divided into edges by Infomap algorithm, which takes characteristics of operation and management of distribution networks into consideration. The validity and rationality of this edge division method are verified in an actual distribution network.
As the key component of regional power grid interconnection, the stability of back-to-back converters is an important prerequisite to ensure the stable operation of regional power grids. In this paper, the stability of back-to-back converter with bidirectional power flow is studied mainly from the perspective of the DC terminal impedance of the converter. The results show that the stability of the back-to-back converter is better when power reverses. To alleviate the stability problem of the back-to-back converter caused by bidirectional power flow, the DC current inner loop is introduced into the inverter to reshape the AC/DC impedance of the inverter, which makes the DC terminal input impedance change from the original negative impedance in the low frequency band to the inductive impedance characteristics, so as to improve the stability margin of the back-to-back converter. Finally, the model of back-to-back converter is established in the Matlab/Simulink simulation platform for simulation verification, and the results verify the feasibility of the theoretical analysis and the proposed control scheme.
The temperature data of non-contact infrared thermal imaging is of great significance for condition monitoring and health assessment of power equipment. However, the high cost and technical barriers of high-resolution infrared imager limit the application of high-resolution thermal imaging in on-line monitoring of equipment in power internet of things. Super-resolution reconstruction meets the resolution requirements and reduces the cost at the same time. In this paper, the enhancement of thermal imaging of power equipment is realized by constructing an improved generative adversarial network of edge enhancement. The network adds a depth residual shrinkage network module on the basis of SRGAN (super-resolution generative adversarial networks), which improves the stability of training on the basis of reducing image noise, enhances the reconstruction of image peak information through edge-extraction technology, and improves the effect of edge restoration. The example analysis shows that, PSNR (peak signal-to-noise ratio ) and SSIM (structural similarity) indicators are used to analyze the overall data and edge data after reconstruction, both are significantly improved, and the subjective visual effect after reconstruction is more clear, which has high engineering practical value.
In the day-ahead electricity market with unilateral quotation, demand side lacks the ability to participate in pricing actively. The price of electricity is affected by the market power of generating units. With the promotion of spot market construction in China's pilot provinces, the next step will be to build an electricity market in both power generation and load participating in bidding. However, due to the particularity of power resources, the advantages of bilateral bidding still need to be quantitatively analyzed. This paper analyzes the domestic and foreign research status of unilateral and bilateral bidding in the existing power market. Facing the problems of unified electricity price on the demand side, the proportion of marketization liberalization and virtual bidding, a multi-period clearing optimization model of day-ahead power market is established considering the above factors. In the transition phase, the demand-side market members are required to declare in the virtual load center node. The influence of bilateral quotation and unilateral quotation on day-ahead electricity market price in different classical scenarios is calculated and compared. Finally, according to the analysis of simulation examples, the effectiveness and feasibility of the clearing model and pricing method are verified, and the bilateral bidding can better promote the power market competition in various scenarios.
In order to solve the problem that the existing transmission network planning methods are not flexible enough in multiple scenarios, and to improve the operation efficiency of the planning methods, this paper proposes a transmission network planning method based on deep reinforcement learning. Firstly, this paper uses scenario information entropy to generate a variety of typical scenarios, and establishes a flexible transmission network planning model suitable for multiple scenarios. Secondly, an improved pointer network model suitable for transmission network planning is proposed by using the deep reinforcement learning method and the actor critical method, and the revised pointer network with Actor-Critic (RPNAC) method is used to solve the planning model. Finally, an example verifies the effectiveness and feasibility of the proposed method.
As the selection of similar days affecting the accuracy of load forecasting, a new short-term load forecasting model using GRA-K-means to select similar days is proposed in this paper. Firstly, GRA is used to select similar days to obtain rough sets of similar days. Secondly, K-means is used to cluster the external factors of rough sets of similar days, and then calculate the Euclidean distance between the predicated days and cluster center. The minimum distance is regarded as the final similar day set. Finally, the final similar day set is applied to train LSTM forecast model of neutral network to load forecast. Compared with LSTM model without similar days or LSTM model using traditional GRA, the MAPE in this paper is partly reduced by 0.911% and 0.637%. The analysis of the results shows that the model using GRA-K-means to select similar days can effectively improve the accuracy of short-term load forecasting.
Large scale wind power integration brings new challenges to the assessment of available transfer capability (ATC) of power system. In order to evaluate the ATC under the uncertain conditions of high wind power permeability more accurately, a method is proposed to evaluate the probability of ATC that takes the correlation of wind power into consideration. Firstly, the historical data are processed using the conditional probability principle and the wind correlation is modeled by the Copula function. Secondly, the evaluation process of ATC involves the calculation of the total transfer capability (TTC) and the existing transfer commitments (ETC), so this paper proposes a bi-level optimization model, on the basis of which, using the Karush-Kuhn-Tucker (KKT) optimal conditions, the bi-level model is transformed into mathematical program with equilibrium constraint (MPEC) model. Then, the MPEC model is transformed into a mixed integer second-order cone planning problem. Finally, the Monte-carlo simulation is used to evaluate the probability of inter-regional ATC, and the IEEE 30-bus system is used as an example to analyze the influence of wind power correlation on the assessment of inter-regional ATC and verify the correctness and validity of the proposed model.
With the massive access of distributed power generation, in order to study the impact of the correlation of distributed wind power on the risk assessment of the distribution network, a risk assessment method for distributed power generations connected to distribution network considering temporal-spatial correlation is proposed. Firstly, considering the temporal-spatial correlation of wind power generators in different seasons, a two-stage wind power scenario generation model considering temporal-spatial correlation in different seasons is established on the basis of Copula function and Markov chain model. Secondly, in order to improve the computational efficiency of risk assessment, the dynamic fault set is established on the basis of the red-black tree structure. By searching and storing the sampled state sequence and its evaluation results, it avoids repeatedly calling the optimal power flow in the same state, and improves the speed of state evaluation. Finally, the operational risk indices such as over-voltage, low-voltage, line overload and loss of load are calculated in IEEE 33-bus distribution network system, and the comprehensive operational risk indices are established on the basis of analytic hierarchy process. The risk caused by strongly correlated wind power connected to distribution network is reasonably and comprehensively evaluated, and the effectiveness and accuracy of the proposed model and method are verified.
