With the implementation of renewable energy quota systems, electricity-selling companies are obligated to consume renewable energy. To study the impact of renewable energy quota systems on the revenue of electricity companies, the market transaction framework and model of electricity companies under such quota systems were analyzed, and a multi-market portfolio transaction model was established. The electricity-selling company considered in the model had different market power purchase costs, flexible contract costs, and electricity-selling incomes. Moreover, it used the method of multiple scenarios for renewable energy power-generation unit output, centralized price bidding in uncertain factors such as market clearing price per month, and the method of conditional value at risk, the relevant risk measurement uncertainty. The model was designed to maximize the profit of the electricity sales company and minimize the market transaction risk. GAMS software was used to solve the model. A simulation analysis of the risk avoidance coefficient, renewable energy unit output uncertainty, renewable energy quota ratio, and other factors affecting the revenue of electricity companies verified the effectiveness of the model, providing theoretical guidance for electricity companies to better participate in market competition under quota systems.
Peak carbon dioxide emissions and carbon neutrality require urgent renewable energy development. This paper proposes a peak regulation control strategy for wind-thermal-storage combined with the green certificate-carbon trading mechanism to ease peak shaving pressure and reduce carbon emissions. The strategy employs a hierarchical control approach. First, the upper model aims to optimize the peak-valley difference of the net load and maximize the revenue from pumping and storage power stations, ensuring their peak-shaving, valley-filling effect, and revenue. Second, the lower model aims at the lowest total operating cost of the system and incorporates a carbon trading mechanism with segmented boundaries and a green certificate mechanism with quantified fines to ensure the economy of the system while meeting the low carbon requirements. Through simulation analysis, the proposed green certificate-carbon trading mechanism control strategy can reduce the output of thermal power units by 1.69% and the total operating cost of the system by 4.09%, verifying the role of the strategy in developing a low-carbon economy.
P2P transactions are a new transaction mode suitable for distributed electricity prosumers participating in the electricity market. Considering the economic benefits to prosumers and the local consumption of distributed clean energy, a P2P electricity transaction process was developed based on a double-round bidding game, and a game model with the goals of economic benefit and clearing energy was established. An example analysis was conducted for a community with different types of distributed electricity prosumers such as residents, offices, and commerce. Compared with continuous bilateral auctions, one-round bidding games, and P2G, the results of P2P transactions with a double-round bidding game showed that economic benefits increased by 19.01%, 28.78%, and 56.81%, and the local consumption rate of distributed clean energy increased by 10.51%, 24.05%, and 85.10%, respectively. In conclusion, adopting a bidding method and strategy can increase the revenue of distributed electricity prosumers, improve the efficiency of P2P transactions, promote the local consumption of distributed clean energy, and help realize the “double carbon” goal.
Lithium-ion batteries, the leading new energy storage technology, have been highly valued and developed. The installed capacity of single-battery energy-storage plants (BESP) continues to grow. Given that a BESP is often composed of many small-capacity standardized energy storage system units, achieving efficient control and management of multiple energy storage unit clusters is an essential guarantee for optimizing the overall performance of energy storage power plants. Cluster control technologies of the BESP were investigated in this study. Based on the BESP control functional requirements analysis, a control architecture and network-layering method based on EtherCAT were proposed. Optimizations were made for the cluster control and management, unit control, and controlled equipment layers, and the communication capability of the BESP control system was calculated. Finally, an experimental platform was set up to verify the effectiveness of the proposed architecture and design process. This provides a technical reference for large-scale BESP to contribute to the rapid and reliable operation of various power systems.
With the introduction of large-scale energy sources and increasing load growth, some substations may face capacity limitations. To ensure the operational economy of power companies, this study undertakes a comparative analysis of two schemes for configuring energy storage power stations and upgrading and expanding substations. First, we considered the economic, reliability, and environmental indicators of the scheme to establish a comprehensive evaluation index system. Second, to maximize the allocation of energy storage benefits as the planning goal, we consider the life cycle cost of the energy storage system and the benefits of improving power grid operation reliability, delaying the upgrading of the power grid, and reducing operating network loss in the distribution network to establish an energy storage cost-benefit model. Subsequently, an improved evolutionary algorithm was utilized in simulations to ascertain the allocation capacity of the energy storage system. The optimal planning scheme was then obtained through the evaluation system. The results show that, under the current grid operation level and mode, the configuration of the energy storage system can delay grid upgrading, improve the overall operation level of the grid, and provide a planning reference for the subsequent large-scale new energy grid connection application of the energy storage system to smooth the new energy output and delay grid upgrading.
Recently, China's carbon market entered its second compliance cycle. The carbon market increases the carbon emission costs of thermal power enterprises to reduce carbon emissions in the power sector. A power system model aimed at optimizing system costs in the power sector is an effective means of simulating the economic impact of the carbon market. In this study, a national plant-level carbon inventory compiled from bottom up is adopted to develop a model of the national power system with carbon market costs, considering the power grid operation balance of all provinces in China under the condition of satisfying new energy consumption. The model can simulate both the regional distribution of carbon market costs of thermal power plants under the current carbon allowance allocation rules and the effect of the current carbon market, reducing carbon emissions in the system. The results show that the benchmark-based allowance allocation mechanism can effectively encourage low-carbon units to effectively generate more electricity, promote optimize the thermal power unit combination using economic means, reduce the emission intensity of the thermal power sector, and promote carbon emission reduction of the national power system.
Power grids in the future will evolve from a single mode driven by a load to a dual-driven mode of a power source and a load. Diversified and flexible resources are urgently required for multiple interactive developments to realize a scenario with a high proportion of renewable energy. The effective identification of evolution-driven paths and the comprehensive optimization of evolution paths have an important guiding significance for clarifying the development direction of future power grids and constructing specific implementation paths. This study analyzed the uncertainty faced by power grid evolution from the aspects of technological maturity, potential, and energy cost and proposed a method for generating massive evolution paths. Subsequently, a data-driven evolution path analysis method was proposed, including path dimensionality reduction and visualization, driving factor identification based on time-varying patterns, and optimal path proposal generation based on the Pareto frontier. Finally, the evolution path of a high-proportion renewable energy system was analyzed using North China as an example. The analysis results indicated that photovoltaics in North China will gradually surpass wind power to become the most important power generation resource in the future and that carbon emissions in 2060 will be 81% lower than those in 2030. The relative importance of each factor differed marginally. At the economic and environmental levels, the most important factor was the price of coal, while the maximum investable capacity of battery energy storage was the main factor at the technical level. Efforts should be made to reduce unit investment in renewable and battery energy storage and coal prices and increase the upper limit of battery energy storage allocation to achieve an evolutionary path that considers both low cost and low carbon emissions.
The current protection scheme for DC microgrids is mainly based on the traditional protection strategy. Because of the large and rapid rising impact current during a DC fault, the available data information for protection is quite rare. This has resulted in high requirements for the rapid detection and breaking capability of protection devices, which in turn considerably increases the construction and operation costs of DC microgrids. In this study, based on a typical DC microgrid, the fault characteristics of voltage source converters such as AC/DC and DC/DC are thoroughly analysed. The study then proposes a control and protection cooperation strategy for a voltage source converter in a DC microgrid during a fault ride-through (FRT). This method designs a universal DC FRT module that can realize quick fault isolation and inject a controllable short circuit current during a DC fault, which reduces the difficulty of protection detection. Simultaneously, the DC FRT module can also quickly restore system operation by automatically identifying whether the fault is rectified. Finally, a simulation model is constructed to verify the effectiveness of the proposed control and protection cooperation strategy.
In recent years, extreme weather events, such as typhoons and rainstorms, have greatly impacted the security of power systems, and the resilience of power systems has gradually become the focus of attention. The resilience of a distribution system is its ability to prevent, resist, respond to, and quickly restore power supply in response to extreme events. This paper proposes a method to improve the resilience of a distribution system by considering the soft open point (SOP) and distributed generation (DG). The DG planning, active islanding, SOP fast fault isolation, and power supply recovery capability are considered in the multi-stage power supply recovery process. First, an SOP mathematical control model and a distribution network fault recovery model considering the SOP are established. Second, a multi-stage resilience evaluation index and method are proposed, a multistage mixed integer linear programming model with SOP is established, and the network topology and operation constraints of each stage are considered. Finally, the effectiveness of the proposed resilient lifting method is verified using the IEEE 33 standard example.
Networked communication is an important component of smart grids. However, the communication delay caused by networked communication further increases the difficulty of maintaining the frequency stability of multi-microgrid systems. Therefore, it is necessary to study the load-frequency control of multi-microgrid interconnected systems with networked delays. To address this problem, a parameter optimization method for a fractional-order proportional-integral-derivative controller based on an improved cooperative quantum-behaved particle swarm optimization algorithm was proposed to improve the performance of controllers under networked communication delays. First, a multi-microgrid interconnected system model with wind and storage characteristics was established. Second, the idea of coordination and chaos was used to improve the quantum-behaved particle swarm optimization to make it suitable for high-dimensional space optimization and optimize the performance of the algorithm in terms of particle swarm diversity and high-dimensional adaptability. Finally, the feasibility and effectiveness of the optimization method were verified from three aspects: the stability limit under different time delays, the dynamic performance under step disturbances, and the CPS index under random disturbances.
To promote the construction of a novel interconnected power system in China—one featuring a high proportion of clean energy access and significant trans-regional power transmission proportion—a new optimal dispatching method is proposed that considers the uncertainty of source load power moment. This approach aims to mitigate the impact of source load uncertainty on cross-regional clean energy consumption and intra-provincial power balance. First, a two-level optimal dispatching mode for an innovative interconnected power system between and within provinces was designed under the planning market dual-track system. Second, the uncertainties of wind and solar power generation and load power were characterized by the moment uncertainty set, and the resulting robust conditional risk measurement model of wind and light abandonment distributions was proposed. Finally, to minimize total power purchase cost between and within provinces, a novel distributed robust, coordinated optimal dispatching model for an interconnected power system was established, and the model was transformed into a semi-definite programming problem, more readily solvable by applying the dual optimization theory. Numerical simulation results show that the proposed model promotes the cross-provincial and cross-regional consumption of clean energy in the western region, reduces the operational cost of the receiving-end system, and effectively improves the ability of the system to handle uncertain fluctuations in the source-load power.
As an essential load-side regulation resource in a high proportion of renewable energy power systems, determining the feasible region of the thermal storage tank temperature of an electric boiler affects system security and is closely related to wind power consumption. However, accurate calculation models for these two factors are lacking. This study proposes a method for fuzzy determination of the feasible region of the water tank temperature and optimal scheduling of the electric boiler system. First, the characteristic models of the hot water storage tank, heating network, and building under different operating conditions in an electric boiler system were established. Second, using the fuzzy control algorithm combined with the initial temperature of the hot water storage tank and the period during which the wind power was more significant than the electrical load, the daily upper temperature limit of the hot water storage tank was calculated, and the optimal scheduling model of the electric boiler system was established. Finally, a simulation example is constructed to verify the effectiveness of the proposed method based on the electric boiler system used in the microstation of an education bureau in Changchun City. The results show that the proposed method can mobilize the building load side to participate in wind power consumption, reduce the risk of fouling of the electric boiler system, and reduce the operating cost of the system.
Frequent occurrences of extreme weather events, such as typhoons, pose challenges to the development of maintenance plans for offshore wind turbines. Periodic maintenance cannot promptly deal with the impact of severe weather such as typhoons, while condition-based maintenance is infeasible for practical operation owing to random maintenance periods. Periodic maintenance plans for offshore wind turbines require the careful consideration of various factors. Therefore, considering the impact of typhoons, this paper proposes a two-level maintenance optimization strategy: the upper level periodic maintenance optimization considers the comprehensive score of the maintenance cost, transmission capacity of offshore wind power systems, and reliability of the receiving-end grid as optimization objectives; the lower-level condition-based maintenance optimization considers the minimum maintenance cost as the goal and optimizes the arrangement of the condition-based maintenance plan above the upper-level periodic maintenance optimization. Subsequently, an example is provided to compare and evaluate the proposed two-layer maintenance strategy. The example shows that, compared with periodic maintenance and condition-based maintenance, the proposed double-layer maintenance plan can effectively improve the reliability of the receiving-end grid and the transmission capacity of offshore wind power systems with a small increase in maintenance costs.
The main control objectives of wind energy conversion systems include reducing mechanical load, increasing power generation, and suppressing power fluctuations. It has been established that under continuous wind fluctuations, forced torsional vibrations exist on the drivetrain in a broad frequency band, which has nearly the same spectrum as wind fluctuations. Moreover, it has been found that existing active damping control schemes cannot cope with forced torsional vibration, which has adverse effects. This study focuses on the multi-objective generation optimization control of wind turbines under the action of continuously fluctuating wind speeds. Based on the virtual configuration principle of frequency division shafting electrical damping with reduced low-frequency bands and enhanced characteristic frequencies, the small signal frequency domain analysis method is used to reveal the influence of controller parameters on the three optimization objectives of torsional vibration, maximum power point tracking (MPPT), and power fluctuation. Considering the constraints of MPPT and power fluctuation on the stabilization of broadband torsional vibrations and the comprehensive optimization of the three aforementioned objectives, the controller structure and parameters are designed for different operating modes in the full wind speed range, and a complete set of control strategies is formed through control synthesis. The hardware-in-the-loop simulation of the controller confirms that the proposed control strategy can effectively achieve multi-objective power-generation optimization control under continuous wind fluctuations.
Four power balance states are proposed for a power system with high penetration of new energy by analyzing the principles of power, electricity balance, and climbing balance. A reliability index that considers climbing flexibility is studied, which extends the applicability of the original reliability index. On this basis, the starting capacity of the system is determined by load correction to ensure that the power demand and climbing demand of the system are satisfied simultaneously, and sufficient hot backup is reserved. A joint probability density model of the unit, load and climbing demand is established, and production simulation technology is used to calculate the proposed indicators. Finally, a reliability test example is used to create a variety of new energy proportion scenarios to verify the effectiveness of the proposed indexes and methods. The results show that in the context of a high proportion of new energy, traditional reliability indexes are conservative, and it is recommended to use reliability indexes that consider flexibility.
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