VPP Self-Scheduling Strategy Using Multi-Horizon IGDT, Enhanced Normalized Normal Constraint, and Bi-Directional Decision-Making Approach

This paper presents a new robust self-scheduling strategy for virtual power plants (VPPs) considering the uncer-tainty sources of electricity prices, wind generations, and loads. Multi-horizon information-gap decision theory (MH-IGDT) as a non-deterministic and non-probabilistic uncertainty modeling framework is proposed here to specifically model the uncertainty sources considering their various uncertainty horizons. Since each uncertain parameter tends to optimize its uncertainty horizon competitively for a particular value of the uncertainty budget, the proposed MH-IGDT model is formulated as a multi-objective op-timization problem. To solve this multi-objective problem, en-hanced normalized normal constraint (ENNC) method is pre-sented, which can obtain efficient uniformly-distributed Pareto optimal solutions. The proposed ENNC includes augmented nor-malized normal constraint method and lexicographic optimiza-tion technique to enhance the search performance in the objective space. To address the unsolved issue of being risk-averse or risk-seeker for a VPP in the market, a bi-directional decision-making approach is presented. This decision maker comprises an ex-ante performance evaluation method and a forward-backward dy-namic programming approach to hourly find the best Pareto so-lution within the generated risk-averse and risk-seeker Pareto frontiers. Simulation results of the proposed self-scheduling strat-egy are presented for a VPP including dispatchable/non-dispatch-able units, storages, and loads

Resiliency-oriented operation of distribution networks under unexpected wildfires using multi-horizon information-gap decision theory

Extreme events may trigger cascading outages of different components in power systems and cause a substantial loss of load. Forest wildfires, as a common type of extreme events, may damage transmission/distribution lines across the forest and disconnect a large number of consumers from the electric network. Hence, this paper presents a robust scheduling model based on the notion of information-gap decision theory (IGDT) to enhance the resilience of a distribution network exposed to wildfires. Since the thermal rating of a transmission/distribution line is a function of its temperature and current, it is assumed that the tie-line connecting the distribution network to the main grid is equipped with a dynamic thermal rating (DTR) system aiming at accurately evaluating the impact of a wildfire on the ampacity of the tie-line. The proposed approach as a multi-horizon IGDT-based optimization problem finds a robust operation plan protected against the uncertainty of wind power, solar power, load, and ampacity of tie-lines under a specific uncertainty budget (UB). Since all uncertain parameters compete to maximize their robust regions under a specific uncertainty budget, the proposed multi-horizon IGDT-based model is solved by the augmented normalized normal constraint (ANNC) method as an effective multi-objective optimization approach. Moreover, a posteriori out-of-sample analysis is used to find (i) the best solution among the set of Pareto optimal solutions obtained from the ANNC method given a specific uncertainty budget, and (ii) the best resiliency level by varying the uncertainty budget and finding the optimal uncertainty budget. The proposed approach is tested on a 33-bus distribution network under different circumstances. The case study under different conditions verifies the effectiveness of the proposed operation planning model to enhance the resilience of a distribution network under a close wildfire. © 2022 The Author(s

Multi-Stage Fuzzy Load Frequency Control Based on Multi-objective Harmony Search Algorithm in Deregulated Environment

A new Multi-Stage Fuzzy (MSF) controller based on Multi-objective Harmony Search Algorithm (MOHSA) is proposed in this paper to solve the Load Frequency Control (LFC) problem of power systems in deregulated environment. LFC problem are caused by load perturbations, which continuously disturb the normal operation of power system. The objectives of LFC are to mini small size the transient deviations in these variables (area frequency and tie-line power interchange) and to ensure their steady state errors to be zero. In the proposed controller, the signal is tuned online using the knowledge base and fuzzy inference. Also, to reduce the design effort and optimize the fuzzy control system, membership functions are designed automatically by the proposed MOHSA method. Obtained results from the proposed controller are compared with the results of several other LFC controllers. These comparisons demonstrate the superiority and robustness of the proposed strategy

Day-ahead allocation of operation reserve in composite power systems with large-scale centralized wind farms

This paper focuses on the day-ahead allocation of operation reserve considering wind power prediction error and network transmission constraints in a composite power system. A two-level model that solves the allocation problem is presented. The upper model allocates operation reserve among subsystems from the economic point of view. In the upper model, transmission constraints of tielines are formulated to represent limited reserve support from the neighboring system due to wind power fluctuation. The lower model evaluates the system on the reserve schedule from the reliability point of view. In the lower model, the reliability evaluation of composite power system is performed by using Monte Carlo simulation in a multi-area system. Wind power prediction errors and tieline constraints are incorporated. The reserve requirements in the upper model are iteratively adjusted by the resulting reliability indices from the lower model. Thus, the reserve allocation is gradually optimized until the system achieves the balance between reliability and economy. A modified two-area reliability test system (RTS) is analyzed to demonstrate the validity of the method.This work was supported by National Natural Science Foundation of China (No. 51277141) and National High Technology Research and Development Program of China (863 Program) (No. 2011AA05A103)

Discovering Communities for Microgrids with Spatial-Temporal Net Energy

Smart grid has integrated an increasing number of distributed energy resources to improve the efficiency and flexibility of power generation and consumption as well as the resilience of the power grid. The energy consumers on the power grid, e.g., households, equipped with distributed energy resources can be considered as “microgrids” that both generate and consume electricity. In this paper, we study the energy community discovery problems which identify energy communities for the microgrids to facilitate energy management, e.g., load balancing, energy sharing and trading on the grid. Specifically, we present efficient algorithms to discover such communities of microgrids considering both their geo-locations and net energy (NE) over any period. Finally, we experimentally validate the performance of the algorithms using both synthetic and real datasets