Abstract
Globally, renewable energy sources (RES) are getting more and more competitive even without subsidies. In general, optimization methods are used to identify the most economic setup of individual power systems. This study contributes to the discussion on how much reserve capacity a power system should have to ensure reliable electricity supply in assessing the explicit and probabilistic system reliability metric loss of load hours (LOLH) as well as expected energy not served (EENS) within a dynamic programming approach. Multi-year RES profiles from different locations are used to identify the minimum reserve margin (RM) requirements using LOLH and EENS as planning criterions. The findings indicate, that using RM as the only reliability constraint within optimization is not appropriate as a too high assumption on RM would increase the required conventional generation capacity unnecessarily and a too low assumption would risk reliable power supply. Using LOLH as the single metric for reliable power system planning, the EENS would grow with increasing RES contribution. This is the result due to the concept of LOLH as the amount of electricity not supplied is not part of the metric, only the hours of power undersupply are. On the other hand, a constant assumption of EENS is misleading as well as the concept of EENS does not consider the number of hours the power service can’t be fulfilled. Therefore, the recommendation is to use LOLH and EENS simultaneously in a single optimization framework as shown within this study.
Highlights
Fluctuating renewable energy (Solar Photovoltaic and Wind) is currently cost competitive with new build conventional power generation in large parts of the world [1]
The additional role of renewable energy sources (RES) in contributing to the peak demand fulfillment of the power system, or in other words, avoiding additional conventional power plants to be built for fulfilling the same peak demand is quite well understood
Stimulated by Newell et al [7], this study assesses the security of supply through a dynamic programming approach, in which the reserve margin (RM) and expected energy not served (EENS) are results to fulfill the predefined reliability level expressed in loss of load hours (LOLH)
Summary
Fluctuating renewable energy (Solar Photovoltaic and Wind) is currently cost competitive with new build conventional power generation in large parts of the world [1]. The purpose of GEP is to minimize required investment for new generation capacity and operational costs, including fuel costs to maximize the power plant owner’s profit under a defined reliability metric target. The role of renewable energy sources (RES) in mitigating fuel consumption is well-known. It is known that the ELCC from RES lessens as its penetration of the system’s capacity increases. It is important to understand the cost compromise between the savings on fuel with increasing penetration, and the ELCC of the additional capacity added under the same reliability target [5]
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