Impact of forecast errors on expansion planning of power systems with a renewables target

Abstract

Highlights• A two-stage market properly models the effects of forecast errors on system operation • Expansion models are formulated as stochastic single/bilevel programming problems • Production forecast errors have a high impact on power system expansion planning • A market that efficiently handles forecast errors involves cheaper expansion plans• The consequences of disregarding forecast errors depend on the market design Abstract This paper analyzes the impact of production forecast errors on the expansion planning of a power system and investigates the influence of market design to facilitate the integration of renewable generation. For this purpose, we propose a programming modeling framework to determine the generation and transmission expansion plan that minimizes system-wide investment and operating costs, while ensuring a given share of renewable generation in the electricity supply. Unlike existing ones, this framework includes both a day-ahead and a balancing market so as to capture the impact of both production forecasts and the associated prediction errors. Within this framework, we consider two paradigmatic market designs that essentially differ in whether the dayahead generation schedule and the subsequent balancing re-dispatch are co-optimized or not. The main features and results of the model set-ups are discussed using an illustrative four-node example and a more realistic 24-node case study.

Unit Commitment Using Nearest Neighbor as a Short-Term Proxy

“…The considered outages in each zone are in transmission lines with ID 2,3,4,5,11,25,26. Visualization is found in Fig.…”

“…Notice this day-ahead problem does not probabilistically account for possible realtime balancing market realizations. Our formulation, often referred to as inefficient market [11], is deterministic given forecast value y s . This is in accordance with the purpose our UC proxy is serving: estimate long probabilistic paths based on multiple deterministic day-ahead solutions, which serve either as samples for higher-level statistics or as a baseline for finer-grained hourly simulation such as in [4].…”

Chance-Constrained Outage Scheduling Using a Machine Learning Proxy

“…In our mid-term problem that spans over a whole year, such an approach will result in high variance and possibly necessitate an intractable number of samples to produce a decent evaluation of scenario costs. The second category of approaches is based on snapshot sampling of static future moments [11]. The main issue with this methodology is the loss of temporal information.…”

A Consensus-ADMM Approach for Strategic Generation Investment in Electricity Markets

“…Among various decision-making problems in power systems, generation investment problems are one of the most complex to tackle from the computational point of view. They need to comprehensively account for different sources of uncertainty, including short-term (e.g., renewable production) and long-term (e.g., demand growth) [1]. They are even more complicated in a market environment due to uncertainty induced by market participation strategies of competing producers [2].…”