The transition to green energy is reshaping the energy landscape, marked by increased integration of renewables, distributed resources, and the electrification of other energy sectors. These changes challenge grid security, particularly regarding the N-1 security criterion, a crucial factor in preventing blackouts. This necessitates studying the security constrained optimal power flow (SCOPF) problem with multiple line outages (N-k). Conventional methods exhibit poor scalability as k increases. This paper proposes a constraint-driven machine learning (ML) approach using line outage distribution factors (LODF). The method shows promise in its ability to scale effectively to N-k contingencies. Key contributions include a deterministic approach for N-k security and a probabilistic security assessment. Case studies on the IEEE 39-bus and the IEEE-118 bus systems show the approach’s effectiveness in identifying violating post-contingency cases, with up to 173x speedups and close to optimal dispatch costs.@en

With the increasing penetration of renewable power sources such as wind and solar, accurate short-term, nowcasting renewable power prediction is becoming increasingly important. This paper investigates the multi-modal (MM) learning and end-to-end (E2E) learning for nowcasting renewable power as an intermediate to energy management systems. MM combines features from all-sky imagery and meteorological sensor data as two modalities to predict renewable power generation that otherwise could not be combined effectively. The combined, predicted values are then input to a differentiable optimal power flow (OPF) formulation simulating the energy management. For the first time, MM is combined with E2E training of the model that minimises the expected total system cost. The case study tests the proposed methodology on the real sky and meteorological data from the Netherlands. In our study, the proposed MM- E2E model reduced system cost by 30% compared to uni-modal baselines.@en

The privatization of distribution systems has resulted in the development of multiple-microgrid (multiple-MG) systems where each microgrid independently operates its local resources. Moreover, the high integration of independent distributed energy sources could lead to operational issues such as grid congestion in future distribution systems. Therefore, this paper provides a transactive-based energy management framework to operate multiple-MG distribution systems; while, alleviating grid congestion in a decentralized manner. In this respect, alternating direction method of multipliers (ADMM) is considered to develop an operational framework that copes with distributed nature of multiple-MG systems. In this context, a novel procedure in the context of ADMM is proposed to distributedly determine transactive coordinator signals which address energy prices as well as power losses and grid congestions. Furthermore, each MG takes into account stochastic programming and the conditional value-at-risk index to handle the uncertainty of its operational scheduling. At last, the proposed framework is applied on IEEE 37-bus and 123-bus test grids to investigate its efficacy in distributed energy management of multiple-MG systems.

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Presence of microgrids (MGs) and renewable energy sources (RESs) in distribution power systems could result in dramatic changes in operation and planning of these systems. In this regard, operational procedures employed by utilities should take into account the intermittent nature of RESs, while coping with the independent operation of MGs. In this perspective, this paper develops a distributed power management framework based on alternating direction method of multipliers (ADMM) for distribution networks with multi-MG (MMG) structures. The proposed approach develops a transactive signal in the context of ADMM to coordinate operation of MGs in a distributed manner. In this context, the configuration of MMG distribution systems is devised in a way that facilitates modern distributed control of the system by eliminating the role of a central coordinator in traditional distribution systems. Moreover, robust optimization technique is deployed in the MG operational scheduling to handle the uncertainty associated with RESs. Finally, through a numerical case study, the efficiency of the proposed framework to schedule local resources for MMG distribution systems is analyzed.

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Restructuring and privatization in power systems have resulted in a fundamental transition of conventional distribution systems into modern multi-agent systems. In these structures, each agent of the distribution system would independently operate its local resources. In this regard, uncertainties associated with load demands and renewable energy sources could challenge the operational scheduling conducted by each agent. Therefore, this paper aims to develop a distributed operational management for multi-agent distribution systems taking into account the uncertainties of each agent. The developed framework relies on alternating direction method of multipliers (ADMM) to coordinate the operational scheduling of the agents in a distributed manner. Moreover, a robust optimization technique is employed to consider the worst-case realization associated with the operation of each agent. Finally, the proposed framework is implemented on IEEE 37-bus network to analyze its efficacy in distributed robust operational management of distribution systems with multi-agent structures.@en

Distribution networks are undergoing a fundamental transition due to the expansion of flexible resources as well as renewable energy sources in the system. In this regard, multi-agent structures are developed in modern distribution systems to facilitate the independent operation of local resources. Nevertheless, the non-coordinated operation of independent agents could result in a deviation between the real-time power purchased from transmission network and the day-ahead scheduling. Consequently, this paper aims to provide a novel framework that enables the decentralized management of multi-agent distribution systems, while coordinating the real-time power request and the day-ahead scheduling. In this regard, the alternating direction method of multipliers (ADMM) is taken into account to facilitate the decentralized operation of the multi-agent systems. Furthermore, transactive control signals are employed to exploit the real-time operational scheduling of independent agents in order to minimize the deviation of real-time power exchange and the day-ahead scheduling. Finally, the developed methodology is implemented on the IEEE 37-bus test system in order to analyze the effectiveness of the proposed approach for the operational management of multi-agent distribution systems.

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In this paper, a two-stage charging management framework is proposed to exploit the potential flexibility of Electric Vehicles (EVs). The aim is to reduce drastic variations in distribution system net load caused by integration of intermittent renewable distributed generation (DG) units. In the first stage, a home-based charging method is formulated in which the desired charging schedule of EVs is obtained by minimizing the cost of energy taking into account owners' preferences. The attained charging schedules are then announced to EVs Coordinator Agent (ECA) which, as the second stage, applies an LP optimization to reduce the cost of ramp provision. Moreover, an incentive scheme is considered to motivate EV owners to participate in the controlled charging program which allows the ECA to modify their desired schedules in exchange for financial bonuses. The simulation results show that the proposed framework can efficiently reduce the system net load ramp and associated costs without disturbing individual convenience.

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