Online decentralized tracking for nonlinear time-varying optimal power flow of coupled transmission-distribution grids

The coordinated alternating current optimal power flow (ACOPF) for coupled transmission-distribution grids has become crucial to handle problems related to high penetration of renewable energy sources (RESs). However, obtaining all system details and solving ACOPF centrally is not feasible because of privacy concerns. Intermittent RESs and uncontrollable loads can swiftly change the operating condition of the power grid. Existing decentralized optimization methods can seldom track the optimal solutions of time-varying ACOPFs. Here, we propose an online decentralized optimization method to track the time-varying ACOPF of coupled transmission-distribution grids. First, the time-varying ACOPF problem is converted to a dynamic system based on Karush-Kuhn-Tucker conditions from the control perspective. Second, a prediction term denoted by the partial derivative with respect to time is developed to improve the tracking accuracy of the dynamic system. Third, a decentralized implementation for solving the dynamic system is designed based on only a few information exchanges with respect to boundary variables. Moreover, the proposed algorithm can be used to directly address nonlinear power flow equations without relying on convex relaxations or linearization techniques. Numerical test results reveal the effectiveness and fast-tracking performance of the proposed algorithm.Comment: 18 pages with 15 figure

A review of co-optimization approaches for operational and planning problems in the energy sector

This paper contributes to a comprehensive perspective on the application of co-optimization in the energy sector – tracking the frontiers and trends in the field and identifying possible research gaps – based on a systematic literature review of 211 related studies. The use of co-optimization is addressed from a variety of perspectives by splitting the studies into ten key categories. Research has consistently shown that co-optimization approaches can be technically challenging and it is usually a data-intensive procedure. Overall, a set of techniques such as relaxation, decomposition and linear approaches have been proposed for reducing the inherent nonlinear model's complexities. The need to coordinate the necessary data from multiples actors might increase the complexity of the problem since security and confidentiality issues would also be put on the table. The evidence from our review seems to suggest a pertinent role for addressing real-case systems in future models instead of using theoretical test cases as considered by most studies. The identified challenges for future co-optimization models include (i) dealing with the treatment of uncertainties and (ii) take into account the trade-offs among modelling fidelity, spatial granularity and geographical coverage. Although there is also a growing body of literature that recognizes the importance of co-optimization focused on integrating supply and demand-side options, there has been little work in the development of co-optimization models for long-term decision-making, intending to recognize the impact of short-term variability of both demand and RES supply and well suited to systems with a high share of RES and under different demand flexibility conditions. The research results represent a further step towards the importance of developing more comprehensive approaches for integrating short-term constraints in future co-optimized planning models. The findings provide a solid evidence base for the multi-dimensionality of the co-optimization problems and contriThis work is supported by the National Council for Scientific and Technological Development (CNPq), Brazil. This work has been supported by FCT – Fundaça˜o para a Ciˆencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Optimal Operation of an Integrated Electricity-heat Energy System Considering Flexible Resources Dispatch for Renewable Integration

Large fluctuations may occur on the energy supply and the load sides when large-scale renewable energies are integrated, leading to great challenges in power systems. The renewable power curtailment is especially numerous in the integrated electricity-heat energy system (IEHES) on account of electricity-heat coupling. The flexible resources (FRs) on both the energy supply and load sides are introduced into the optimal dispatch of the IEHES and further modeled to alleviate the renewable fluctuations in this paper. On the energy supply side, three kinds of FRs based on electricity-heat coordination are modeled and discussed. On the load side, the shiftable electricity demand resource is characterized. On this basis, the solution for FRs participating in IEHES dispatch is given, with goals of maximizing the renewable penetration ratio and lowering operation costs. Two scenarios are performed, and the results indicate that the proposed optimal dispatch strategy can effectively reduce the renewable energy curtailment and improve the flexibility of the IEHES. The contribution degrees of different FRs for renewable integration are also explored

Distribution Network Planning and Operation With Autonomous Agents

With the restructured power system, different system operators and private investors are responsible for operating and maintaining the electricity networks. Moreover, with incentives for a clean environment and reducing the reliance on fossil fuel generation, future distribution networks adopt a considerable penetration of renewable energy sources. However, the uncertainty of renewable energy sources poses operational challenges in distribution networks. This thesis addresses the planning and operation of the distribution network with autonomous agents under uncertainty. First, a decentralized energy management system for unbalanced networked microgrids is developed. The energy management schemes in microgrids enhance the utilization of renewable energy resources and improve the reliability and resilience measures in distribution networks. While microgrids operate autonomously, the coordination among microgrid and distribution network operators contributes to the improvement in the economics and reliability of serving the demand. Therefore, a decentralized energy management framework for the networked microgrids is proposed. Furthermore, the unbalanced operation of the distribution network and microgrids, as well as the uncertainty in the operating modes of the microgrids, renewable energy resources, and demand, are addressed. The second research work presents a stochastic expansion planning framework to determine the installation time, location, and capacity of battery energy storage systems in the distribution network with considerable penetration of photovoltaic generation and data centers. The presented framework aims to minimize the capital cost of the battery energy storage and the operation cost of the distribution network while ensuring the security of energy supply for the data centers that serve end-users in the data network as well as the reliability requirements of the distribution network. The third research work proposes a coordinated expansion planning of natural gas-fired distributed generation in the power distribution and natural gas networks considering demand response. The problem is formulated as a distributionally robust optimization problem in which the uncertainties in the photovoltaic power generation, electricity load, demand bids, and natural gas demand are considered. The Wasserstein distance metric is employed to quantify the distance between the probability distribution functions. The last research work proposes a decentralized operation of the distribution network and hydrogen refueling stations equipped with hydrogen storage, electrolyzers, and fuel cells to serve hydrogen and electric vehicles. The uncertainties in the electricity demands, PV generation, hydrogen supply, and hydrogen demands are captured, and the problem is formulated as a Wasserstein distance-based distributionally robust optimization problem. The proposed framework coordinates the dispatch of the distributed generation in the distribution network with the hydrogen storage, electrolyzer, and fuel cell dispatch considering the worst-case probability distribution of the uncertain parameters. The proposed frameworks limit the information shared among these autonomous operators using Benders decomposition

供給と需要側を考慮した電源システムのモデリングと評価

Modelling and optimization of sustainable power system and energy network are becoming complex engineering. Demand side resources also need to be planned considering characteristics of district energy supply scenario. This research first analyzes the feasibility of VPP based on scenario of Chongming Island. VPP focuses on expansion of renewable energy and upgrade of efficient appliances, results verify the effectiveness of the VPP concept. Then investigates the techno-economic viability of high variable renewable integration. PV-PHS dispatch scenarious are carried out with constraints, PHS effectively recovers the suppression and decreases the PV power levelized cost. Introduction PV-PHS shifts merit order curve to right, decreasing power generating cost. Thirdly, cost and environmental benefits of optimal designed decentralized energy systems were investigated. Scheduled distributed energy resources could be optimized to benefit the public grid. Performance of dynamic price is investigated based on the social demonstration project experiment. Finally, the conclusions are provided.北九州市立大

A Bi-level Market-Clearing for Coordinated Regional-Local Multi-Carrier Systems in Presence of Energy Storage Technologies

A multi-energy system (MES) provides greater flexibility for the operation of different energy carriers. It increases the reliability and efficiency of the networks in the presence of renewable energy sources (RESs). Various energy carriers such as power, gas, and heat can be interconnected by energy storage systems (ESSs) and combined heat and power units at different levels (e.g., within a region or a local). Non-coordinated optimization of energy systems at local and regional levels does not verify the whole optimal operation of systems since the systems are operated without considering their interactions with each other. One of the most famous sources of flexibility is ESSs. Hence, this paper presents a stochastic decentralized approach to evaluate the impact of ESSs on regional-local MES market-clearing within a bi-level framework. On the regional level, the economic interaction between the electricity and natural gas (NG) systems is carried out by a centralized system operator (CSO). In addition, coordination between various energy carriers is implemented by the energy hub operator at the local level. To ameliorate the flexibility of the NG system in the regional MES, the linepack model of gas pipelines has been considered. Local MES modeling is performed through multiple input/output ports using a linear energy hub model. The proposed model is a mixed-integer linear programming (MILP), which is solved by CPLEX solver in GAMS software