Applications of optimization models for electricity distribution networks

Abstract

Increased penetration of low‐carbon technologies, such as residential photovoltaic systems, electric vehicles, and batteries, can potentially cause voltage quality issues in distribution networks. Active distribution networks adopt control schemes where these assets are actively managed to prevent potential issues, increasing the network utilization. Mathematical optimization is a key technology in enabling such applications, either directly as the underlying solution, or for benchmarking effectiveness. As networks are operated closer to their engineering limits, models representing distribution network physics become increasingly important. This article reviews how distribution networks are modeled with varying degrees of detail in the context of optimization problems. It goes on to catalog the applications that use such models, and ends with an overview of toolchains to implement them, to enable the transition from the passive to active management of the distribution system. This article is categorized under: Energy Systems Analysis > Systems and Infrastructure

Event-Informed Identification and Allocation of Distribution Network Planning Candidates With Influence Scores and Binary Linear Programming

“…The capacity of the ESS E n,ESS is then calculated by finding the maximum energy that would be required to solve all congestion events in any day, either from demand or generation excess. This is given by the maximum between dailyaggregated demand-caused and generation-caused congestion issues using (8). This approach accounts for two or more subsequent congestion events without enough time for the ESS to charge/discharge back into levels that could address the second congestion.…”

“…Furthermore, distribution networks have many more direct customer connections than the transmission system. Thus, the applicability of transmission network-inspired optimisation algorithms to the DNEP problem is hampered by algorithmic considerations, such as computational intensity [8].…”

“…[7]. While these approaches can consider stochastic or deterministic parameters, integer or continuous variables, they always result in a non-convex problem that is hard to solve exactly in reasonable time [8]. These approaches must decide from a very large set of potential candidates (search space), requiring relaxations and scenario reduction approaches that can potentially misrepresent the complexity of distribution network applications.…”

Review of Smart Transformer-Based Meshed Hybrid Microgrids: Shaping, Topology and Energy Management Systems

“…On the other hand, the set of feasible solutions presented by RO techniques tends to be more conservative than stochastic optimization. However, this depends on the choice of the worst-case scenario to be included in 𝑈, whose adjustment can reduce the risk level according to the quantification of the feasible solution [125], which finally establishes the operating point of the microgrid. Therefore, applying RO techniques for developing the energy management algorithm is proposed based on these two criteria.…”

Computational analysis of impedance transformations for four-wire power networks with sparse neutral grounding

“…Low-voltage (LV) networks are dealing with the integration of new technologies, including solar PV, home batteries and electric vehicles. Solving physics-based mathematical optimization problems in such networks is an interesting technology development opportunity [6], with problems such as unbalanced network state estimation, distribution locational marginal pricing, network-constrained battery dispatch and EV charging coordination receiving significant attention by researchers.…”