Practical recursive algorithms and flexible open-source applications for planning of smart distribution networks with Demand Response

Abstract

-Distribution networks are currently undergoing fundamental changes due to the rise of smart solutions such as for instance Demand Response (DR), which increases network complexity and challenges the adequacy of traditional planning practices. This calls for the use of suitable planning methodologies. However, the planning problem may be too cumbersome for most commercial software tools, or may lead to complex bespoke optimisation models that may not be easy to use by network planners. In this light, this work proposes a recursive function that can be used in practical algorithms for planning of smart distribution networks. The recursive function can emulate business-as-usual planning practices and further optimise them, including DR options as potential substitutes for network reinforcement. Several case studies based on real UK networks highlight the robustness and flexibility of the proposed algorithms to address different problems, including uncertainty analysis and risk management. The results clearly show that the proposed tool leads to increased economic and social benefits, particularly when optimising investment strategies considering smart DR solutions. The model is herein distributed open-source as accompanying material to this paper with the aim of encouraging new practices required for smart distribution network planning beyond the traditional academic applications.

Regulatory capital and social trade-offs in planning of smart distribution networks with application to demand response solutions

“…The engine is based on exhaustive searches to assess all feasible interventions that could be deployed and select the best strategy. See [31][32][33] for a more technical and detailed description of the optimisation engine.…”

“…Detailed descriptions of each network, including capital and social performance (without including trade-off analyses) when networks reinforcements are planned under a wide range of conditions (e.g., different objective functions, system headroom, infrastructure and DR costs, DR availability, uncertainty scenarios, and so forth) can be found in the references [31][32][33][34]36,38].…”

A Decentralized Local Flexibility Market Considering the Uncertainty of Demand

“…In addition, on the long-term planning period, demand flexibility can be a reason to postpone or eliminate the need for upgrading the size of the network. It has been advised by many regulators [33][34][35] that considering demand flexibility in the network planning process can be very beneficial, as it decreases the forecasted peaks, which decreases the capital expenditure needed for network upgrading [36]. Another positive impact of demand flexibility is towards the challenges faced by distribution networks with large-scale penetration of renewable energy resources (RES).…”

Techno-economic assessment of distribution network reliability services from microgrids

“…Based on existing UK regulations, distribution network reliability is evaluated in terms of Customer Interruptions (CI) and Customer Minutes Lost (CML) for events lasting more than three minutes (i.e., shorter interruptions are neglected) [17]. In light of this, sequential Monte Carlo simulations are used to simulate annual CI and CML under baseline conditions where the MG either does not provide reliability support (i.e., Baseline) or provides the service as recommended by the operation model (the model is presented in the next section).…”

“…More specifically, sequential Monte Carlo simulations considering over 17000 scenarios for contingencies throughout the year were used to simulate the reliability benefits attributed to the MG. For this purpose, a typical failure rate of 0.05/km was assumed, and the alternatives to close the NOP and place mobile generators as a means to restore supply to end-users (after contingencies occur) are assumed to take 1h and 5h in average, respectively. As considered in existing UK regulations, CI costs are set at £15.44 per interruption and CML costs are set at £0.38 per minute lost [17].…”