A Game-Theoretic Approach to Model Interruptible Loads: Application to Micro-Grid Planning

Abstract

This paper proposes a novel modeling approach for the efficient integration of demand response (DR) resources into the equipment capacity-planning problem of micro-grids based on Game Theory. The main advantage of this approach is that it determines the DR events based on the day-ahead system state estimates (in contrast to the conventional exogenetic demand-side management approaches), whilst protecting the customers' welfare. A battery-less, 100%-renewable, gridindependent micro-grid is conceptualized, and the town Ohakune, New Zealand is used as a test-case to evaluate the effectiveness of the proposed modeling framework. The numerical simulation results indicate that the proposed approach achieves substantial (12.59%) savings in the life-cycle cost of the target system, as compared to the case where a timeof-use DR is implemented.

Off-Grid Multi-Carrier Microgrid Design Optimisation: The Case of Rakiura–Stewart Island, Aotearoa–New Zealand

“…Principally, the proposed method determines the optimum capacity of an MECM's equipment using the net present cost (NPC) valuations [41], the loss of power supply probability (LPSP) reliability indicator [42], and a state-of-the-art mete-heuristic optimisation algorithm, namely the moth-flame optimisation algorithm (MFOA) [43]. The MFOA is chosen as its superiority to a wide range of well-established and state-of-the-art meta-heuristics in MG capacity planning applications is demonstrated in [25,44], based on rigorous statistical, multi-test-case-oriented analyses. It has also been shown that it is highly stable against the changes in initial guess, and therefore, descriptive statistics analyses, based on several independent simulation runs, are not required [44].…”

“…On the other hand, swarm intelligence-oriented meta-heuristics do not involve any such simplifications of the underlying optimisation problem, but at the cost of relatively substantially higher computational complexities (running times). Yet, despite the fact that the outperformance of meta-heuristics to exact mathematical optimisers has been highlighted in a multitude of energy dispatch and planning optimisation studies [19][20][21][22][23][24][25], the existing meta-heuristic-based approaches in the literature on MECM capacity planning optimisation and, more strikingly, off-grid MECMs, have remained extremely low, as the summary of the most vigorous studies in the literature in Table 1 suggests. The table, furthermore, serves to position this paper within the identified methodological gaps in the mainstream optimal MECM sizing literature.…”

“…The techno-economic specifications of the conceptualised MECM's components are summarised in Table 2 [25,55,56,[58][59][60][61]. The leading brands of equipment in New Zealand's renewable energy asset market were chosen based on the first author's judgement of both prevalence and viability, the costs of which are reported in 2019 New Zealand dollars (NZD) in the table-hence, all cost estimates are cited in 2019 NZD throughout this paper (the 2019 annual average exchange rate: NZD 1 = USD 0.69).…”

Considering Material Cycles for a Transition to Low-Carbon Energy Systems in Aotearoa/New Zealand: A Systematic Review

“…On the other hand, several reviewed studies analysed the infrastructure required for a low-carbon transition in the electricity, transport and building sectors (see Table 1). [11]- [15] Local Reviewed community energy initiatives and reported some of infrastructure planned. [16] Local Optimal number of tidal turbines.…”

“…The reviewed studies have analysed the infrastructure required for renewable energy generation, such as the required number of turbines for a tidal energy power plant [17]. Other reviewed studies estimated the infrastructure required to transition to low-carbon micro-grid electricity systems, not considering the upgrade of the electricity transmission system [11]- [15]. On the other hand, Transpower [19], which operates the national transmission network, presented the current components of the transmission network in the country and future projections to increase electricity transfer and meet future demand.…”