Robust Scheduling Optimization Model for Combined Cooling, Heating and Power System in Industrial Parks

With increasing scale of renewable energy integrated into the power system, the power system needs more flexible regulating resources. At present, besides traditional thermal and hydro power plants, pumped hydro storage and battery storage are the most commonly used resources, and they form a wind-thermal-hydro-storage multi-energy complementary system. This paper proposes an optimal scheduling strategy to dispatch the resources in the multi-energy complementary system. First, models of diverse types of resources. i.e., hydro power, pumped hydro storage, and battery storage, are established. Then, a day-ahead optimization scheduling model is proposed for the multi-energy complementary system. Finally, case study is conducted on a revised IEEE 30 node system. Simulation results demonstrate that the proposed method can fully utilize the characteristics of different kinds of power resources to consume renewable energy and enhance the safety and economy of the multi-energy complementary system.

Guest Editorial: Situational awareness of integrated energy systems

Chapter 4 - Data-driven robust stochastic optimization for power systems operations

The operational optimization of the coal mine integrated energy system (CMIES) is crucial for reducing costs and carbon emissions. However, the system’s multi-objective nature, stringent constraints, and the uncertainty of renewable and mine-derived energy make solving its optimization challenging. Thus, this paper first presents a data-driven uncertainty transformation method to address the uncertainty of renewable energy and mining derived energy output; then, a multi-task multi-objective evolutionary algorithm based on adaptive auxiliary tasks (MMOEA-AS) is proposed, which includes a main task and three auxiliary tasks. Meanwhile, an adaptive update strategy for auxiliary tasks and a matching degree-guided knowledge transfer mechanism are proposed to improve the performance of the algorithm. Finally, taking the energy scheduling problem of a coal mine in Shanxi, China as an example, MMOEA-AS is compared with five advanced evolutionary algorithms. The results show that MMOEA-AS can effectively solve the operation optimization of the CMIES, and obtain the optimal scheduling results.

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.

Cooperative game robust optimization control for wind-solar-shared energy storage integrated system based on dual-settlement mode and multiple uncertainties

Optimal operation strategy for interconnected microgrids in market environment considering uncertainty

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.

Multi-energy complementary renewable energy system is an efficient energy supply system based on thermoelectric-gas-storage coupling technology to realize full renewable energy supply in local regions, with biomass power generation as the basic energy, wind, light and storage as the supplement. Since 2000, China has promulgated a series of policies related to multi-energy complementarity to promote energy reform and renewable energy development. China supports the construction of multi-energy complementarity, micro-grid and renewable energy projects. By optimizing the process route of “source-network-load-storage” and adopting chain and vertical integrated feedback as auxiliary technical measures, the deficiencies of renewable energy projects in energy production, storage, transportation, conversion, use and recovery can be complete. Intelligent dispatch through “end-pipe-cloud” integrated energy management platform can compensate the characteristics of non-flexible operation, uncertainty and volatility of renewable energy. According to the characteristics of renewable energy in multi-energy complementary projects, technical evaluation index, economic evaluation index and social benefit index are established, and the advantages and disadvantages of renewable energy projects are evaluated by multi-dimensional indicators.

Real-time long-term voltage stability assessment based on eGBDT for large-scale power system with high renewables penetration

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.

Virtual power plant (VPP) has gradually become a key technology to the increasing penetration of renewable energy. The uncertainty and variability of renewable energy and load demand pose significant challenges to the efficiency and stability of VPP's operation. In this paper, a data-driven two-stage stochastic robust optimization (SRO) scheduling model is proposed for a VPP considering wind power, solar power, load demand, and market price uncertainties. The objective is to maximize the profit of the VPP in the energy market and minimize the total system cost of the VPP in the reserve market under the worst-case realization of the uncertainties. The Dirichlet process mixture model (DPMM) and variational inference algorithm are employed for constructing the data-driven uncertainty ambiguity set considering the correlations among multiple uncertainties. The tailored column-and-constraint generation algorithm is developed to solve the SRO model iteratively by reformulating the second stage with the application of the Karush-Kuhn-Tucker conditions. Results from a case study illustrate the effectiveness and superiority of the proposed model.

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.

Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: A comparative study

Due to the fossil fuel crisis, combined cooling heating and power (CCHP) systems which can jointly supply electricity and hot/cold have become the mainstream of energy generation technology. In this paper, tradable green certificate(TGC) mechanism is firstly introduced to operation of CCHP system, and the impacts of tradable green certificate on the scheduling of CCHP system are studied. Based on the probability distribution of wind speed, an economic scheduling model is proposed, which considers the uncertainties of renewable energy and tradable green certificate price as well as the constraints of secure operation of power system and pipe networks. In order to address the non-convex scheduling optimization problem, global descent method is applied, which can continuously update the local optimal solutions by global descent function, and find global optimal solution. Finally, one modified IEEE 14-bus system is used to verify the performance of the proposed model and optimization solver.