The integrated energy system (IES) is an effective way to achieve the“carbon neutrality and emission peak”goal. In order to further explore the role of the adjustable potential of demand side on carbon emission reduction, an optimized operation model of IES considering the demand response under the carbon trading mechanism is proposed. Firstly, according to the characteristics of load response, the demand response is divided into two types: price-type and substitution-type. The price-type demand response model is established on the basis of price elasticity matrix, and the substitution-type demand response model is constructed by considering the conversion of electricity and heat. Secondly, base-line method is used to allocate free carbon emission quota for the system, and considering the actual carbon emissions of gas turbine and gas boiler, a carbon trading mechanism for the IES is constructed. Finally, a low-carbon optimal operation model of IES is established, whose objective is to minimize the sum cost of energy purchase, cost of carbon transaction and cost of IES operation and maintenance. The effectiveness of the proposed model is verified through four typical scenarios. By analyzing the sensitivity of demand response, heat distribution ratio of gas turbine and the operating state of the system under different carbon trading prices, it is found that reasonable allocation of price-type and substitution-type demand response and heat production ratio of gas turbine is beneficial to improve the operating economy of the system. Making reasonable carbon trading price can realize the coordination of system economy and low carbon.
Due to the fluctuation and intermittence of new energy output, its direct access to the grid will affect the safe and stable operation of the power system. In order to promote renewable energy consumption, developing hydrogen storage system coupled with electricity and hydrogen is an effective way. For this reason, aiming at the optimal configuration of hydrogen storage capacity of new energy power station at the power generation side, a multi-objective optimal configuration model of hydrogen energy storage is established with the minimum investment cost of hydrogen energy storage, the minimum system cumulative tracking plan error and the maximum increment of carbon dioxide emission reduction as the objective function, and the abandonment rate and the actual site area as the constraints. The model is solved by the combination of genetic algorithm with elitist strategy and entropy weight method. Finally, the effectiveness of the proposed model and algorithm is verified by a case study in Gansu Province, China. The results show that the optimal number of 200 kW electrolytic cells, 6 kg hydrogen storage tanks and 200 kW fuel cells are 268, 291 and 222, respectively. The hydrogen storage system can actively respond to the dispatching command and greatly reduce the power abandonment rate.
In order to deal with the problem of low economy caused by the uncertainty of renewable energy output and the single energy supply form of traditional microgrid, this paper proposes a two-stage stochastic robust optimization model for multi-energy microgrid. The model considers the grid structure of the power grid and the heating network. The objective function is to minimize the two-stage microgrid cost in the worst wind power output scenario, which includes the start and stop costs of the first stage and the operating costs of the second stage. Because the decision-making and optimization results of the first stage and the second stage influence each other, the two-stage optimization problem is difficult to solve directly. This paper uses a stochastic robust optimization framework for linear decision-making to solve the model. Firstly, the related theories of linear decision-making methods are applied to transform the second stage. Secondly, the cone-shaped fuzzy set is used to describe the uncertainty of renewable energy output. Finally, the“sup-min”problem in the second stage is derived as the“min”problem of cone optimization, and then combined with the“min”problem in the first stage to obtain the single-layer cone optimization problem which can be solved directly, and the optimal solution is obtained by using the solver. The simulation results verify the effectiveness of the proposed model and method.
The calculation of the optimal energy flow (OEF) of the integrated electricity-gas system (IEGS) is the basis of the optimal planning and operation analysis of IEGS. There are some problems in the existing methods for solving the OEF of the IEGS, such as frequent data interaction, poor convergence and difficulty in ensuring privacy. Therefore, a method for calculating the OEF applying parameter linear programming is proposed in this paper. Firstly, the optimal power flow (OPF) models for the power network and natural gas network are constructed, respectively. The OPF model of the power network adopts the optimal DC power flow model considering the active power loss. The OPF model of the natural gas network is established on the basis of the second-order cone relaxation. Secondly, the correlation function between the electricity-gas coupling power and the optimal solution of the power flow is constructed based on the parameter linear programming theory. Then, the correlation function is transferred to the natural gas system for joint optimization, and returns the electricity-gas coupling power information to the power system for solution. Thus, the natural gas flow and power flow results of the OEF are obtained. Through the simulation analysis, the method proposed in this paper can accurately solve the OEF through one-time information exchange. At the same time, the amount of interactive information is small and able to protect private information. It is suitable for the decomposition calculation of the OEF.
As the energy market gradually opens up, multiple market factors have been involved in the traditional centralized optimal dispatching approach. In this context, the paper takes the optimal operation of community integrated energy system (CIES) as an application scenario and proposes a source-load collaborative optimization model under the guidance of an integrated energy service provider (IESP). On the demand-side, an integrated demand response (IDR) strategy is established according to the transferable electricity load and gas load as well as the virtual heat storage characteristics of the enclosed structures in the buildings. On the supply-side, an integrated energy service provider is introduced to replace the energy networks and lead the collaborative and flexible trade of multiple energies. Besides, it is able to comprehensively evaluate the community’s energy demand, respond feedback and the situations of interactive power of major grid and tie lines in the community so as to optimize the electricity-gas combined price signals. The bi-level optimization algorithm of particle swarm optimization combined with mixed integer linear programming is used to optimize the price signal of the upper service provider and the demand response and economic dispatching of the lower community. Considering the mutual influence in the process of bilateral interaction between supply-side and demand-side, the interactive strategy of all parties in pursuit of interest goals is solved by cycle iteration. Numerical simulation shows that the model can be used to exploit response potential in the participation satisfaction of the community and energy economy, realize“peak-shaving and valley filling”in the community and the main network, optimize the configuration of energy resources, and increase the overall economy of the system.
Applying the state space modeling method, the small disturbance models of different wind farms connected to the power grid through paralleled AC and DC lines are established, and the generation mechanism of various oscillation modes in the system is analyzed in detail. Furthermore, the differences of the effects of system structure parameters and key controller parameters on the natural oscillation mode and coupled oscillation mode of the system are analyzed. On this basis, the interaction mechanism and factors between AC and DC system are revealed. The correctness of the system is verified by time-domain simulation on PSCAD / EMTDC platform. The results show that the influence of system parameters on the coupled oscillation modes between AC and DC systems is complex.
In order to reduce the sensitivity of AC line differential protection in flexible DC transmission system of inverter new energy station, an optimization scheme of differential protection based on improved criterion is proposed. Firstly, according to the AC line fault characteristics of the flexible DC transmission system of the inverter new energy station, the adaptability of the AC line differential protection is analyzed. In case of fault in the normal area, the braking component is set to 0 to improve the sensitivity of the protection. When the phase angle difference on both sides exceeds 90°, the current phase angle difference on both sides is added to the criterion to reduce the reduction of sensitivity. Then, compared with the method of directly reducing the braking coefficient, the optimization scheme can take into account the reliability of differential protection by changing parameters, and the optimization scheme is not affected by the strength of the external system of the inverter new energy station. Finally, the 4-terminal flexible DC system model of inverter new energy station connected to Zhangbei is built in PSCAD simulation software, and the simulation verifies the effectiveness of the optimization scheme.
LCL filter is widely used in grid-connected inverter system for its advanced filtering performance. However, the inherent resonance characteristic of LCL filter and digital control delay will seriously endanger system stability. To increase the system robustness, passive damping method is usually applied to suppress the filter resonance in engineering. Since the filter resonance problem is affected by many factors, the selection standard of damping resistor is unclear. In order to design the damping resistor accurately and efficiently, this paper introduces the index of“critical damping factor”and analyzes two typical passive damping methods: damping resistor in series with filter capacitor or in parallel with filter capacitor. When system damping factor is larger than the critical damping factor, the resonant peak of the amplitude frequency characteristic curve can be completely suppressed and the stability of grid-connected inverter system is fully guaranteed with the minimum cost of passive damping losses. Moreover, the simulation of three-phase grid-connected inverter is carried out in PLECS and the simulation results verify the effectiveness and correctness of the theoretical analysis.
In the low-voltage microgrid, due to the influence of the impedance of the line and the impedance mismatch, the conventional droop control often has the problems of power coupling and uneven distribution of steady-state reactive power. Aiming at the above problems, an improved droop control method with adaptive coefficients is proposed. This strategy adjusts the equivalent line impedance to inductive by introducing the reference virtual reactance, weakening the coupling problem caused by the line resistive component, so that inductive droop control can be applied; secondly, low-bandwidth communication is introduced, and the droop is adaptively adjusted according to the power sharing requirements. Coefficient is adjusted to eliminate the problem of line impedance mismatch, so as to achieve accurate and even distribution of reactive power. Compared with traditional droop control, this method is suitable for microgrid control under any line impedance condition, and has good dynamic and steady-state performance. The simulation results in Matlab/Simulink prove the correctness and effectiveness of the method.
Distributed energy storage (DES) has flexible operating characteristics and can be properly configured to effectively serve the voltage management in the active distribution network. The existing studies on voltage management-oriented DES optimization configuration are usually focused on the balanced network model to analyze the impact of energy storage operation characteristics on the system voltage, while the real distribution networks are actually unbalanced. To this end, a method for sequence optimization of DES in unbalanced distribution networks applying voltage sensitivity analysis is proposed, and the optimal configuration of DES in unbalanced distribution networks is studied from the perspective of improving the network voltage. Firstly, the DES access location is determined according to the comprehensive voltage sensitivity analysis; secondly, taking into account the economy of system operation, the access capacity of DES with the goal of minimizing the investment and maintenance cost of DES is optimized. Finally, by improving the IEEE 33-node three-phase distribution network to carry out a calculation example analysis, it is verified that the proposed DES sequence optimization configuration method can effectively improve the voltage quality of the unbalanced distribution network.
Large-scale wind power connected to the power grid leads to the decrease of inertia response capacity and frequency-regulation reserve capacity of the power grid, and then the frequency stability of power grid is decreased. A method for energy storage (ES) configuration helps wind power units to provide the same inertia response capacity as equal capacity synchronous generator is proposed. The aim is to realize that the inertia response capacity of the grid and the overall frequency-regulation capacity of the system remain unchanged before and after wind power units instead of the synchronous generator connecting with the grid. On this basis, wind power units have different frequency-regulation ability under different wind speeds. Furthermore, a coordinated control strategy of combined wind and storage system is proposed, which can not only provide inertia response support at low wind speeds, but also assist wind power units to recover their rotating speed at medium wind speed to avoid secondary frequency drops. The simulation results show that energy storage with the capacity equalled to only 5% of the rated power of the wind farm can compensate the inertia response capacity of the power grid, as well as the power needed to restore the wind power units’ speed, which greatly improves the operation frequency stability of the power grid.
With the high integration of power grids and communication networks, false data injection attack has become a security hazard to the current power grids. In order to achieve the higher security level of power system in context of cyber-physical fusion, firstly, a bi-level attack model of false data inject attack under cyber-physical power system is proposed. The upper model represents the attack strategy of attacker, while the lower model represents the response of the power grid after attack. The model is solved using KKT conditions. Secondly, a rapid screening method with iterative ideas is proposed, which can quickly screen the vulnerable lines under the false data injection attack. Thirdly, a multi-line attack strategy is proposed to help grid operators conduct a more in-depth evaluation of the grid. Finally, the rationality of the attack model and the feasibility of the rapid screening method are verified through the simulation on the IEEE 39-bus system and IEEE 118-bus system.
The accurate prediction of the root alarms in the electric power communication network can assist the operation and maintenance personnel to efficiently investigate and quickly locate the high-risk points of the communication network in advance, then avoid regional communication failures and derivative alarms from the root, and reduce network risks and operation and maintenance costs. Aiming at the redundancy of source data and low accuracy of root alarm prediction in the existing research, this paper proposes a prediction model based on APRIORI-Bayesian optimization XGBoost for root alarms of electric power communication network. The APRIORI algorithm is used to optimize the input of the prediction model and mine the association rules among the influencing factors of root alarms. With the aid of the probabilistic method of association rules, the key influence factors are determined to reduce the training data redundancy of the Bayesian optimized XGBoost model, increase the data value density, and then improve the model efficiency and warning prediction accuracy. Then the prediction model is constructed on the basis of the Bayesian optimized XGBoost algorithm with the key factors. Finally, the experimental results show that the proposed algorithm performs well in prediction accuracy, recall and F-value, and achieves the optimal prediction accuracy when the minimum support is 15%, which can provide technical support for efficient maintenance and troubleshooting of power communication network.
In view of the low degree of participation in electricity market transactions of concentrating solar power (CSP) in China, market-related operation research is still blank. This paper considers CSP as the main body of the market and is aimed at the electricity spot market transaction. According to the analysis of its internal“light, heat, and electricity”energy flow characteristics, an operation model of a CSP station with thermal energy as the energy center is constructed. Then, with the goal of maximizing the electricity sales revenue of CSP, a market transaction decision model based on the multi-oligarch Cournot model is constructed, and the solution process of its nonlinear complementary method is given. Finally, taking an actual CSP station as a reference, in three typical daily scenarios, an example simulation of CSP participating in electricity market transactions is carried out. The results show that: due to its excellent stable output, compared with traditional PV, CSP has a significant improvement in market competitiveness, which can largely make up for its lack of capacity cost. The surplus heat of CSP can better satisfy the demand for electro-thermal coupling system, replace cogeneration links, reduce operating costs, and meet the development needs of the integrated energy system in the future.
In the context of the large-scale access of clean energy and the increasing pressure of peak-shaving power grids, energy storage devices and deep peak-shaving thermal power units, as important adjustment resources to promote the absorption of new energy and suppress the peak-valley difference of the power grid, have been widely concerned by academia and industry. Considering the characteristics of multi-type energy storage devices and combined with the deep peak-shaving characteristics of thermal power units, this paper compares and analyzes the economic benefits of different types of energy storage devices assisting deep peak-shaving of thermal power units. Firstly, by fully considering the technical, economic characteristics and development prospects of multi-type energy storages, the most representative three kinds of energy storage systems: pumped storage, compressed-air energy storage and lithium-ion battery energy storage are selected, and the relevant system operation models are established. Secondly, considering the deep peak-shaving characteristics of thermal power units, the operation model and economic model of thermal power units are established. Thirdly, considering the life-cycle cost of multiple-type energy storage systems, a day-ahead economic dispatch model of multi-type energy storage systems considering the deep peak-shaving of thermal power units is constructed, and a comparative analysis of the economics of multi-type energy storage is carried out accordingly. Finally, on the basis of the typical daily data in a certain area, a simulation example is carried out to compare and analyze the economic benefits of multi-type energy storage power stations, and the recommendations of energy storage capacity configuration are put forward.
