Designing Electricity Distribution Networks: The Impact of Demand Coincidence

TL;DR

The paper addresses how temporally coincident electricity demand from technologies such as electric vehicles and heat pumps affects long-term investments in distribution networks. It introduces the distribution network reconfiguration problem with line-specific demand coincidence (DNRP-LSDC), a design model that incorporates a line-specific coincidence factor $\gamma(|\Gamma_j|)$ and allows unconstrained network layouts, with exact solutions for small instances and heuristics for larger ones. Empirical analysis on 74 Swiss real-world networks shows average investment costs increase by about 84% under high coincidence, with larger and rural networks most affected; synthetic-network experiments reveal systematic structure-dependent effects and robustness to parameter variations. The work offers methodological advances and practical algorithms (e.g., MST/EW initialization, Tabu Search, PECA) to support strategic and operational network design, informing policy and budgeting to mitigate coincidence-driven cost burdens.

Abstract

With the global effort to reduce carbon emissions, clean technologies such as electric vehicles and heat pumps are increasingly introduced into electricity distribution networks. These technologies considerably increase electricity flows and can lead to more coincident electricity demand. In this paper, we analyze how such increases in demand coincidence impact future distribution network investments. For this purpose, we develop a novel model for designing electricity distribution networks, called the distribution network reconfiguration problem with line-specific demand coincidence (DNRP-LSDC). Our analysis is two-fold: (1) We apply our model to a large sample of real-world networks from a Swiss distribution network operator. We find that a high demand coincidence due to, for example, a large-scale uptake of electric vehicles, requires a substantial amount of new network line construction and increases average network cost by 84 % in comparison to the status quo. (2) We use a set of synthetic networks to isolate the effect of specific network characteristics. Here, we show that high coincidence has a more detrimental effect on large networks and on networks with low geographic consumer densities, as present in, e. g., rural areas. We also show that expansion measures are robust to variations in the cost parameters. Our results demonstrate the necessity of designing policies and operational protocols that reduce demand coincidence. Moreover, our findings show that operators of distribution networks must consider the demand coincidence of new electricity uses and adapt investment budgets accordingly. Here, our solution algorithms for the DNRP-LSDC problem can support operators of distribution networks in strategic and operational network design tasks.

Figures (6)

• Figure 1: Temporal electricity demand of two consumers with different degrees of demand coincidence. The aggregated peak demand $D$ in the electricity network depends on the coincidence factor $\gamma$. Left: Under a high demand coincidence, peak demands of consumers occur at similar points in time. Right: Under lower demand coincidence, the temporal overlap of the individual consumer peak demands is smaller.
• Figure 2: Example with two coincidence factors $\gamma(N)$ as a function of $N$ consumers.
• Figure 3: Flows in an exemplary solution of the DNRP-LSDC for an instance with three vertices.
• Figure 4: Initial layouts generated for an example network by the MST algorithm (top left). The MST returns the network with the lowest branching. Afterward, the Esau-Williams algorithm is applied with decreasing values for $K$. For the Esau-Williams algorithm, the branching increases with smaller values for $K$. In the case of $K=1$, the star network is returned (bottom right). The search process is stopped once a feasible solution is found.
• Figure 5: Example real-world network with buildings, roads, and landscape (left) and pre-processed network layout (right) with transformer (large dot) and consumers (small dots).

Theorems & Definitions (10)

• Remark 1
• Remark 2
• Proposition 1: Line capacity ratios
• Corollary 1
• Theorem 1: \ref{['pro:ratio']}.
• Theorem 2: \ref{['pro:min']}.
• Theorem 3: \ref{['pro:star']}.
• Proposition EC.1
• Corollary EC.1
• Proposition EC.2