This paper proposes a risk-averse time-series joint scheduling method to release inherent benefits for improvements on operational cost, voltage profile and risk control, through flexibility extraction from grid-storage-demand resources in active distribution networks (ADNs). In particular, a flexibility analytical framework is developed to fully harness the controllability of various resources in both spatial and temporal scales, and is available for future analysis. The expected operational cost and the risks imposed by uncertainties are simultaneously addressed via conditional value at risk while satisfying physical and operating constraints, in which a sample weighted average approximation (SWAA) technique is employed for approximating the faced uncertainties. The SWAA-based stochastic scheduling problem is further transformed into a second-order cone programming problem via linearization and conic relaxation. Numerical simulations on 33-bus and 123-bus test systems verify the effectiveness of the proposed method.

Flexibility Provisions in Active Distribution Networks With Uncertainties

Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks

Electricity distribution is moving towards active, more flexible, smarter and decentralized energy systems. This transition requires System Operators (SO) to dynamically monitor and control the power flow across the network. Demand Response (DR) can be considered as an alternative solution to the costly investment of upgrading conventional Distribution Networks (DN). Hence, the role of DR as a considerable potential of elastic demands in the Active Distribution Network Management (ADNM) is vital. The aim of this paper is to review the recent literature and pilot implementations towards residential DR activation and applications at the electricity distribution level. Background concepts, DR programmes and key participants in ADNM are explained. DR activation strategies for residential demand responsiveness at the network level are categorized and discussed together with the challenges and future directions of this technology. The most relevant DR innovation trials in Great Britain (GB) and their outcomes are also discussed.

Residential Demand Response Strategies and Applications in Active Distribution Network Management

With the increase in the complexity of the topology of transmission and distribution systems, associated with the predictability in the management of the dispatch of prosumers, new techniques for state estimation, and application of metaheuristics are necessary. In the current work a pilot project in Greece that addresses the difficulties of congestion and balancing management that system operators face in the renewable energy sources era, in accordance with the OneNet’s architecture is described. Available resources of grid’s flexibility are identified, and the implementation of an integrated monitoring system based on weather conditions with an energy control and dispatch system in the Greek electricity grid is addressed. The document suggests that flexibility resources will derive through predictions that have been improved and efficient forecasts from increased spatial resolution Numerical Weather Predictions and integration of Artificial Intelligence preventing the power system of entering dangerous topological or operational states.

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Forecasting Transmission and Distribution System Flexibility Needs for Severe Weather Condition Resilience and Outage Management

Future research tendencies for solar energy management using a bibliometric analysis, 2000-2019.

In this paper, a model-based predictive control method is proposed for utilization of flexible resources such as battery energy storage systems and heating systems effectively to provide demand response in low-voltage distribution networks with solar photovoltaic. The contributions of this paper are twofold. First, a linear power flow method based on relaxation of branch power losses applicable to radial distribution networks is proposed and formulated. Second, a flexible resources controller that solves a multi-objective linear optimization problem in receding-horizon fashion is formulated taking into account system states, forecasts of generation, and loads. Using the proposed control algorithm, flexibility from network resources can be utilized for low-voltage network management with assurance of quality of service to the customers. Simulations are conducted for summer and winter cases on a simplified Danish low-voltage network using Matlab/Simulink to study the performance of the proposed control method. Compared to the methods in state of the art, the proposed linear power flow method is proven to be accurate for the calculation of network power flows. Simulation results also show that proposed flexible resources controller can meet the network control objectives while satisfying the network constraints and operation limits of the flexible resources.

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Nainar Karthikeyan

Papers

Predictive Control of Flexible Resources for Demand Response in Active Distribution Networks