Optimal operation strategy for interconnected microgrids in market environment considering uncertainty

With the embryonic form of the new power system emerging, the uncertainties of renewable energy and load have brought challenges to power generation and transmission planning, making it imperative to construct a coordinated planning model for "source-grid-load-storage". Meanwhile, in the environment of the power spot market, the extensive integration of renewable energy has increased the uncertainties and complexities of the system, imposing higher requirements on the coordination among "source-grid-load-storage". Under such circumstances, this paper constructs an expanded power generation and transmission planning model based on the power spot market. Considering the uncertainties of generation and load, a coordinated planning model for "source-grid-load- storage" in the power market environment is proposed. Through chance constraints, the adaptability of the planning scheme to the random uncertainties of generation and load is coordinated, thus effectively handling the two uncertainty factors. Finally, a provincial power grid in East China is taken for simulation analysis on the model to verify its scientific nature, effectiveness, and feasibility.

With the market-oriented reform in China's energy field, the operating model of the microgrid has changed. This paper studies the microgrid operation strategy under the new power reform system, and establishes the electricity transaction optimization model of the interconnected microgrid in the power market. The model can coordinate the power generation of each microgrid power component, schedule the amount of energy traded among microgrids in interconnected microgrid system, and optimize thee electricity sold by interconnected microgrid systems and other members of the electricity market. Steps using genetic algorithm to solve the operational strategy are given by this paper. To evaluate the performance of proposed method, simulation analysis of multiple microgrids in different operating modes and scenarios is given at the end of the article.

Power deficiency management is an important factor in the operation of remote microgrids (MG). Load-shedding is traditionally considered as the main mechanism to manage the network under power deficiency conditions. To minimize load-shedding, islanded neighboring MGs can be connected to each other in remote areas to provide a self-healing capability. For this, extra generation capacity needs to be available in the distributed energy resources (DER) of one of the MGs to supply the extra demand in the other MG. In this way, the total load in the system of interconnected MGs will be shared by all the DERs within those MGs. This paper presents a strategy which aims to interconnect two neighboring microgrids in remote areas to minimize the necessity of load-shedding. This strategy also needs to decide when two interconnected MGs should be isolated. This paper focuses on the self-healing strategy, its criteria and features. The presented algorithm in this paper does not need any data communication system for its operation. The performance of the developed technique is validated by PSCAD/EMTDC simulations.

This paper presents a hierarchical control of an interconnected DC microgrid system. In an interconnected DC micrgrid, power injections and absorptions at different buses can cause significant voltage deviations at load terminals. Even though droop control at the primary level aids in sharing of load among the converters, it introduces load dependent voltage drop in the system. To overcome this, voltage restoration can be provided at the voltage controlled buses. However, secondary voltage control at different buses cannot ensure voltage quality at critical load buses. This paper proposes a tertiary load voltage control utilizing the coordination among different voltage controlled buses to set their references such that the load voltage deviation is minimized. Based on the present state of the network, the tertiary level runs an optimization algorithm and reroutes the power distribution in the network. This redistribution of power in turn mitigates the load voltage deviations. The proposed control topology is verified using simulation results for a four-bus interconnected DC microgrid system.

The multi energy complementary system is a new power energy technology Firstly, we studied renewable energy and load uncertainties of an operation optimization system, and established the industrial park energy system, which includes wind power, photovoltaic power, a combined cooling, heating and power system, and an energy storage tank. Secondly, given the renewable energy uncertainties of unit output and load, we introduced a robust multi-objective operation optimization method for industrial park energy supply systems while considering conservative system operation. Thirdly, we examined the synergetic and game relationship among multiple objectives. The particle swarm optimization algorithm is was used to optimize the system operation scheme, reduce the feasible domain, and improve the efficiency of the solution. Finally, the simulation results show that the operation optimization method effectively uses the demand response to optimize economic and environmental objectives and ensure the optimal operation efficiency of the system under multiple uncertainties.

Achieving stable operation of multi-microgrid (MMG) systems with complete privacy protection is a challenging problem, which is rendered more difficult when considering both the nonlinear conditions of the equipment and the uncertainty of renewable energy. To address these issues, this study proposed a novel data-driven method. First, the neural network was used to describe the relationship between the partial load and the efficiency and carbon emission factors of the equipment. Second, the least squares-generative adversarial networks (LS-GAN) and Monte Carlo were used to calculate the output interval of wind power and photovoltaics. Consequently, a method for calculating the normal distribution model based on historical information was proposed. Finally, a multi-agent soft actor-critic (MA-SAC) was proposed to realize peer-to-peer energy trading and privacy protection for the MMG system. The results of a case study show that the proposed method is sufficiently flexible to solve the problem of MMG optimization operation considering the uncertainty of renewable energy under nonlinear and nonconvex constraints. In addition, MA-SAC exhibits stronger optimization capabilities than the current mainstream multi-agent deep reinforcement learning algorithms.

Coordinated Optimal Allocation of DGs, Capacitor Banks and SOPs in Active Distribution Network Considering Dispatching Results Through Bi-level Programming

This study endeavours an effective frequency control of renewable‐based isolated two‐area interconnected microgrid (ICμG) without battery, incorporating wind power generation in area‐1, dish‐Stirling solar power generation system in area‐2 and other common distributed generation systems such as diesel engine driven generator, plug‐in hybrid electric vehicle, heat pump and freezer in both the areas. A recently developed heuristic optimisation technique called water cycle algorithm (WCA) is applied to optimally tune the parameters of the non‐integer fractional‐order proportional‐integral‐derivative (FOPID) controllers employed with ICμG. Application of WCA‐based FOPID controllers in frequency control two‐area ICμG without battery is a novel work. The comparative performance analysis of proportional‐integral‐derivative (PID), PID with filter and non‐integer order‐based FOPID controller with their gains tuned by particle swarm optimisation (PSO), improved PSO, firefly algorithm and WCA algorithms indicates the superiority of WCA‐based FOPID controller's under different case studies (considering different load disturbances, wind speed variation with real data, and solar irradiance) in terms of frequency deviation, tie‐line power, and objective function. Furthermore, the sensitivity analysis contemplates that WCA‐optimised FOPID controller can withstand ±25% change in synchronising coefficient, +20% change in loading condition and ±25% change in frequency bias constant without resetting the gain values.

Microgrid can allow a lot of renewable energies to penetrate into power system and achieve maximum utilization of existing energies and demand-side management. Existing study focus on the uncertainty of renewable energy and load, but the microgrid risk status is rarely considered. The paper proposes an microgrid optimal dispatch model based on situation awareness (SA). According to the three levels of SA, the uncertainty model in microgrid is established, the risk coordination constraints are analyzed, and the risk cost is proposed. The proposed model takes two cases of loss of load and resource wastage into consideration to cover most possible impacts of potential risk in microgrid. A typical microgrid is analyzed to find optimal decisions and to minimize the comprehensive risk. The validity of the proposed model and the impact of confidence level and tolerance margin is demonstrated through case study.

Chance-constrained co-optimization of peer-to-peer energy trading and distribution network operations

Carbon peaking and carbon neutrality goals have put forward new requirements for the development of sustainable energy. As an important part of the energy system, the power system plays an important role in achieving sustainable energy development. Future power systems will be sustainable, with a high proportion of renewable energy such as photovoltaic and wind power. With advancements in technology and the sustainable development of power systems, the construction costs of wind power, photovoltaics, and other renewable energy sources will continue to decline. At the same time, in order to cope with the uncertainty of renewable energy, more flexible resources need to be built. Under a high proportion of sustainable energy penetration, the utilization rate of flexible resources is low, which will lead to an increase in adjustment costs. Economic issues have an important impact on the development of new power systems, and power system construction will in turn affect social and economic development. Therefore, it is necessary to analyze changing trends in electricity prices in the process of power system sustainability from a quantitative perspective, so as to provide guidance for future planning. In view of the above problems, this paper takes Gansu Province as an example, predicts future power installation trends, by analyzing the power system form evolution, and uses production operation simulation technology to determine the power generation and consumption situation. On this basis, the costs of various power supplies and power grids are calculated, and trends in electricity prices in Gansu Province are forecast.

Setting retail electricity prices is one of the significant strategies for energy management of multi-microgrid (MMG) systems integrated with renewable energy. Nevertheless, the need of privacy preservation, the uncertainties of renewable energy and loads, as well as the time-varying scenarios, bring challenges for pricing problems. In this paper, a two-level pricing framework is proposed based on interval predictions and model-free reinforcement learning to address these challenges. In particular, at the higher level, the distribution system operator (DSO) is viewed as an agent, which sets retail electricity prices without detailed user information for privacy protection to maximize the total revenue from selling energy with reinforcement learning. For time-varying scenarios with intermittent photovoltaic power generation and diverse loads, a differentiable trust region layer is considered in reinforcement learning to improve the robustness of the policy updating process. While at the lower level, operators in microgrids solve three-phase unbalanced optimal power flow (OPF) problems to minimize generation cost and network power loss. Additionally, to deal with the challenges from the uncertainties of renewable power generation and user loads, interval predictions are chosen to quantify prediction errors and improve the flexibility of pricing policies. Finally, a set of experiments are conducted to validate the effectiveness of the proposed method for pricing problems in MMG systems.

With the continuous development and construction of the new power system, there would be a large number of renewable energy connected to the electric network in the future. And the uncertainty of renewable energy would have a greater impact on the power grid, such as excessively large investment and low utilization rate. In view of this, this paper fully considers the adjustable comprehensive resources of the power supply and the load side, and proposes a novel transmission network planning framework under the new power system. Further, from the aspects of economy and reliability, an evaluation system of novel transmission network planning framework is established. At last, taking a municipal 220 KV power grid as an example, the novel transmission network planning framework is applied, and the effectiveness of the proposed planning framework is verified.

With the increase of single-phase devices and loads in the power system, the three-phase unbalance problem of the low voltage distribution network is more serious. Besides, the intermittency and volatility of renewable energy increase system uncertainty. To address this issue, this paper proposes a phase switch device placement model for the low voltage distribution network with renewable energy sources. The proposed model combines network reconfiguration and phase switch device to mitigate the three-phase unbalance. Considering the uncertainty of loads and renewable energy output, the proposed model is formulated as a mixed integer linear programming model based on stochastic programming, which can be solved by general commercial solvers and find the global optimal solution. The effectiveness of the proposed model is validated on the IEEE 13 node test feeder. The results show that the proposed model can significantly mitigate the three-phase unbalance of low voltage distribution network.

This study explores the management of a system of multiple interconnected microgrids in a loop configuration. To facilitate renewable integration and reduce the cost of operation, an economic dispatch model of the distributed energy resources (DER) with dynamic update of the states of the energy storage systems (ESS) to handle the forecasting error in the photovoltaic (PV) power output is developed. By simulating the economic dispatch of DER for various PV output profiles, the minimum size of ESS required in a microgrid can be determined. The sizing and placement of ESS in a loop configuration, be it one ESS for each individual microgrid or one ESS for the whole interconnected system, are also explored. The case study conducted found that the total capacity of the ESS for the interconnected microgrids in a loop configuration with a single shared ESS is smaller than that for the loop configuration with individual ESS, which is in turn smaller than that for the islanded mode.