Renewable energy sources such as solar energy and biomass energy are abundant in rural areas of China, but the energy utilization rate is low and pollution is serious. Aiming at the rural scene where clean renewable energy sources such as biomass and solar energy are connected, a multi-objective planning method based on the coupling utilization of biomass and solar power for rural electricity-heat integrated energy system is proposed, considering the transmission characteristics of heat network and the adjustable agricultural production load. Firstly, a typical architecture of rural integrated energy system is constructed considering the coupling utilization of biomass and solar energy resources. At the same time, the virtual energy storage function of thermal network, adjustable load of agricultural production and key equipment of the system are modeled and analyzed, and relevant operation strategies are proposed. Secondly, a planning and optimization model for rural integrated energy system is constructed, which takes into account economy, environmental protection and energy efficiency. Considering constraints such as investment capacity, equipment operation and energy balance, the Levy flight-based particle swarm optimization algorithm (LPSO) is used to solve the planning optimization scheme. Finally, the simulation is carried out in a village and town of a county in northern China, and the results show that the proposed method is reasonable and effective.
In order to reduce the power supply cost of the multi-energy industrial park with 5G base stations, this paper proposes a life-cycle energy supply system planning method for the multi-energy industrial park. Firstly, the multi-energy load model of 5G base station is established and the coupling characteristics of multi-energy flows in the park are described. Then, taking the lowest life cycle costs as the objective function, the planning model of the industrial park is constructed. Further, considering the uncertainty of distributed photovoltaic power generation and multi-energy loads in the park, and applying the chance constraints to describe the influence of uncertainty on the planning scheme, the robustness of the planning scheme is improved. The simulation examples shows that the annual energy cost of the park is reduced by 0.52 % and the annual planning investment cost is reduced by 7.6 % through multi-energy complementarity in the park, which verifies the effectiveness of the proposed method. In addition, the robustness and economy of the planning scheme can be effectively balanced by adjusting the over-limit probability parameters of chance constraints in the model.
In order to further promote the renewable energy consumption and carbon emission reduction of the park integrated energy system (PIES), and optimize the total operation cost of the PIES, this paper proposes a PIES optimization scheduling model considering the recovery of waste heat from power to gas (P2G) and demand response under the carbon-green certificate trading mechanism. Firstly, the stepwise carbon trading mechanism and green certificate trading system are considered to promote the consumption of renewable energy and carbon emission reduction. Secondly, the P2G equipment to recover waste heat is introduced, and the electric-heat-gas coupling relationship is optimized to improve the system's renewable energy consumption and carbon emission reduction level. Then, a demand-side response model including reduced, transferred and substituted loads is established to guide users to change their energy use mode from high energy consumption and high pollution to low-carbon and sustainable. Finally, the PIES optimization scheduling model is constructed to minimize the sum of energy purchase cost, wind curtailment penalty cost, demand response compensation cost, stepped carbon trading cost and green certificate trading revenue. The simulation results show that the model can effectively promote the renewable energy consumption and carbon emission reduction and reduce the system operation cost.
Considering the fluctuation of distributed generation output, the error of load forecasting and the difference of operation characteristics of various scheduling resources in time scale, in this paper, a multi-time scale optimal dispatching model of active distribution network under demand-side response mechanism is proposed. Firstly, the forms of load participating in demand-side response are divided into price type and incentive type, and the two forms of demand-side response are modeled and analyzed separately. Secondly, a multi-time scale scheduling framework based on model predictive control for active distribution network is established. On the basis of the model predictive control method, three optimal dispatching models of daily distribution, intraday rolling and real-time feedback are established separately. Considering the coupling characteristics of the active and reactive powers in the process of distribution network dispatching, the voltage of each node of distribution network is controlled within the allowable range by controlling reactive power dispatching resources. Finally, through the simulation of the improved 31-node example, it is verified that the proposed model can effectively reduce the prediction error and improve the economy and safety of active distribution network operation.
The bus load has small base, high volatility and uncertainty. With the access of renewable energy such as photovoltaic and wind power, the bus load is increasingly disturbed by random factors such as weather, and high precision forecasting of bus load is greatly affected. Aiming at the problem of bus load forecasting in small-sample scenario, this paper proposes a short-term bus load forecasting method based on discrete wavelet transformation-multiple objective slime mould algorithm-support vector machine (DWT-MOSMA-SVM). Firstly, discrete wavelet transform is used to process bus load data. Then giving consideration to the accuracy and stability of the forecasting, multiple-objective slime mold algorithm (MOSMA) is used to optimize the penalty factor and kernel function parameters of SVM. Finally, selecting the Pareto optimal solution on the Pareto front, SVM forecasting model is built for training. The forecasting results are compared with those of LSTM, un-optimized SVM and MOSSA-optimized SVM model. Experimental results show that the proposed MOSMA-SVM model has better forecasting accuracy and stability.
In recent years, the increasing strong typhoon weather has brought more and more serious losses to the distribution network in coastal and some inland areas, resulting in large-scale power loss of important load for a long time. Improving the recovery capacity of active distribution networks containing multiple sources and loads has become an urgent problem to be solved. To deal with the problem that the existing power outage evaluation methods of distribution networks failed to obtain the information of the nodes, this paper proposes a novel evaluation method of power outage in active distribution networks containing multiple sources and loads on the basis of deep learning method, such as the Transformer model. Considering the ground roughness and height, meteorological information correction model of the geographical environment of the active distribution network is constructed combined with the typhoon disaster meteorological data under the condition of weak communication. On this basis, considering the disaster mechanism of strong typhoons and the topology of active distribution networks, the Transformer model is used to construct the power outage model of active distribution networks to improve the accuracy of power outage evaluation in active distribution networks containing multiple sources and loads. Through the simulation tests of the improved IEEE 33-node active distribution network, it is verified that the proposed power outage evaluation method for active distribution networks can meet the accuracy requirement of power outage evaluation in typhoon-prone distribution networks.
In view of the interaction between the power supply unit (PSU) inside the data center power supply system and the power distribution network, it is easy to cause low-frequency oscillation in the power supply system of the data center. At present, there is no effective analysis model for the research on the low-frequency oscillation of parallel multiple PSUs in the data center. Therefore, this paper proposes a low-frequency admittance modeling and stability analysis method for parallel multiple PSUs in data centers. On the basis of the low-frequency admittance model of the single-phase PSU, a low-frequency admittance model of parallel multiple PSUs considering the frequency coupling effect and the influence of the DC-side voltage is established by equivalently aggregating the frequency coupling effects of parallel multiple PSUs to improve the accuracy of the impedance model. The low-frequency admittance model of single-phase PSU in the parallel system; and according to the Nyquist stability criterion, the influence of the number, capacity and voltage-loop parameters of the PSU on the system stability is analyzed in detail, and the influence law of each factor on the system stability is obtained. The effectiveness of the proposed method is verified by simulation, and a reliable analysis model is provided for the planning, design and stable operation of the data center power system.
Accurate identification of distribution network fault causes is of great significance to shorten the time of fault search and improve the speed of power supply recovery and power supply reliability. According to the attribution of responsibility, the causes of distribution network fault can be divided into internal causes and external causes. Internal causes refer to electrical related causes such as equipment insulation weakening and overvoltage, while external faults usually refer to weather, animal or human activities. Faults caused by external causes are the result of a combination of many factors. In this paper, a fault external cause identification method is proposed, which integrates the non-electrical information such as line parameters, weather and time, and the electrical information such as fault current and phase number. Firstly, the characteristics and related influencing factors of five typical external fault causes are analyzed to build the basis of identification model. Then unsupervised learning training is used to obtain the optimal parameters of each layer of deep belief network, and supervised learning is used to fine-tune the global parameters, and the classification model of external causes of distribution network faults based on deep belief network is obtained. Finally, the accuracy of the algorithm is verified by using the actual fault data of a certain area in western China. The result shows that the recognition accuracy can reach 94.82%, which proves the correctness of the method.
The use of on-site renewable energy is beneficial to the data center to reduce energy costs and carbon emissions, but the intermittent output of renewable energy will lead to the ineffective use of some on-site renewable energy. According to the adjustable characteristics of data center load, this paper designs a joint renewable energy consumption mechanism between data center operators and electric vehicle operators, so as to achieve the utilization of residual renewable energy. According to Stackelberg game theory, this paper fully considers the competitive preference behavior of multiple subjects, and establishes a single model of energy service providers' consignment and a multiple model that considers the combination of direct electricity sales and energy service providers' consignment. According to the demand of daily charging load of electric vehicle charging station and the comparison of advantages of the two modes, a joint optimization strategy for energy consumption of data center and electric vehicles considering competitive preference is finally proposed. Simulation and analysis results show that the optimization strategy adopted in this paper has good economic benefits.
With the continuous increase of renewable energy penetration, the volatility and randomness of the power grid are increasing, and demand-side resource supply will become more and more important. Electric vehicles (EVs) account for a relatively large share of demand-side resources, but existing studies have rarely considered the multi-factor interaction of individual and social factors in the process of EV participating in demand response. Therefore, an EV multi-scenario demand-response charging scheduling strategy based on the decision-making trial and evaluation laboratory-adversarial interpretive structure modeling method (DEMATEL-AISM) is constructed in this paper. Firstly, a data mining method is used to analyze the operating characteristics of charging stations and EV charging characteristics in multiple scenarios, and an EV charging load characteristic model is constructed. Secondly, the DEMATEL-AISM algorithm is used to analyze the multi-factor coupling relationship affecting EV charging behavior in multiple scenarios, and the dominant factors are explored. Finally, according to the analysis of the dominant factors in multiple scenarios, a user regulation strategy is formulated under the influence of multiple factors. Through simulation, it is verified that the method proposed in this paper can effectively smooth out the peak and valley levels of load, node voltage fluctuations is reduced, the stability and economy of the demand side of the power system is improved.
In an integrated electricity-gas system (IEGS), the rapid growth in power and gas load has led to not only the voltage instability in power system, but also gas pressure drops in natural gas system. It further results in the instability in gas pressure and makes an impact on the security and stability of IEGS. Thus, it is necessary to find how power and gas load growth influences the gas pressure in IEGS. Draw on the idea of power system static sensitivity analysis and on the basis of the unified IEGS multi-energy flow model, this paper constructs two sensitivity matrixes for gas pressure-active load and gas pressure-gas load. The continuous multi-energy flow model is then used to calculate the sensitivity index of gas pressure-total load when the IEGS reaches the stability limit of gas pressure. The weak line of gas-pressure in IEGS can be located according to the index. Finally, results of a case study show the effectiveness of the proposed method in locating the gas pressure weak line of the IEGS.
To improve the dynamic performance of the microgrid frequency response, a microgrid frequency control strategy based on the cooperative control theory is proposed. According to the microgrid frequency characteristics and the frequency-regulation characteristics of the micro-sources, macro variables containing system dynamic performance and control indexes are built on the basis of cooperative control theory, and the cooperative control law of the microgrid frequency regulation is derived, then a microgrid cooperative controller is designed. The energy storage device quickly participates in frequency regulation when the system is disturbed and reduces the deterioration of frequency by releasing or absorbing power. The diesel generator then regulates the power of the microgrid according to the change of energy storage power and system frequency to ensure the reliability of power supply on the micro-source side. Finally, a simulation platform based on MATLAB/Simulink software is built to verify the proposed control strategy and to compare and analyze the microgrid frequency regulation effect under different control methods. The simulation results show that the proposed cooperative controller can fully utilize the frequency-regulation characteristics of the micro-source to quickly smooth out the frequency fluctuations caused by power imbalance in the microgrid when the microgrid power is disturbed.
Aiming at the stability problem of source-load cascade system with constant power load in paralleled Boost DC/DC converter in DC network, the stability of the paralleled Boost DC/DC converters and the load capacity with constant power load are analyzed by eigenvalue method and impedance ratio method. The correctness of the theoretical analysis is verified by the time-domain simulation method, and the advantages and disadvantages of V-I and I-V droop control for the load power sharing are systematically compared in this paper. For multiple paralleled Boost DC/DC converters with resistance load, the stability region of controller parameters does not change with the increase of the number of paralleled units. The decrease of virtual impedance in V-I droop system, or the increase of virtual impedance in I-V droop system will lead to the deterioration of system stability. For multiple paralleled Boost DC/DC converters with constant power load, the change of the load capacity of the system is consistent with the stability. With the same virtual impedance, the load capacity of multiple paralleled V-I droop system is greater than that of I-V droop system. This paper provides general design rules for two types of droop control in practical engineering applications of parallel Boost DC/DC converters.
The development of renewable energy is a fundamental measure to resolve environmental pollution and energy crisis, and achieve the carbon peaking and carbon neutrality goals. However, the inherent volatility and fluctuation of renewable energy output bring unprecedented challenges to the planning and operation of the power grid. This paper studies the optimal ratio of renewable energy and energy storage, aiming to minimize power fluctuation. According to the complementary nature of wind and solar resources, the mode of optimal ratio of wind and solar power that leads to minimal power fluctuation is established and is further transformed into linear programming. The optimization problem of energy storage capacity aiming to smooth the renewable energy output is formulated as a multi-parameter linear program, where storage charging power and energy capacities are parameters. The power fluctuation index is expressed as an analytical function in storage parameters, which is convex and piecewise linear. On the basis of the fluctuation index function, the optimal storage capacities can be determined according to the costs. The proposed method provides an illustrative tool and more abundant information for policy and decision-making.
The coastal areas of China are economically developed and have a large energy demand. It is of practical significance to explore the production of hydrogen using offshore wind power to supply energy to other industries. This paper compares two utilization modes of offshore wind power after transmission on shore, i.e., direct sales and use for hydrogen production. Firstly, by comparing the power characteristics of alkaline (ALK) and proton exchange membrane (PEM) electrolyzers, the hydrogen production model is established. Secondly, the economic models of the two modes are established. Finally, the economy of different wind energy utilization modes are compared using net present value (NPV) and levelized cost of hydrogen production (LCOH). The results show that, under the current technical scenario, when the hydrogen price is 46.93 yuan/kg and the feed-in electricity price of wind power is 0.5318 yuan/(kW·h), the wind power used for hydrogen production is more economical than the wind power sales mode. The hydrogen price is the biggest factor affecting the economics of the hydrogen production model, where the hydrogen price depends on the supply and demand relationship in the future hydrogen market, and there is great uncertainty.
