Robust optimisation for deciding on real‐time flexibility of storage‐integrated photovoltaic units controlled by intelligent software agents

Abstract

The increasing penetration of Renewable Energy Sources (RES), the liberalization of the electricity markets across the world and devices such as smart meters present the end-users of the power system with new opportunities to decrease their electricity costs or become active electricity market participants. However, the intermittent nature of RES and dynamic electricity prices require tools against uncertainty to protect the end-users from underutilizing their assets. In this work, we examine the effectiveness of Robust Optimization (RO) in maximizing the economic benefit of the owner of a home battery storage system in the presence of uncertainty in dynamic electricity prices. The advantage of the robust model is that it keeps its linear class, thus it is not too computationally intensive to be included in the control algorithm of a residential energy storage controller. In the usecase, the aggregating entity makes requests for flexibility and coordinates 100 such devices using a price signal, while the storage controller is doing dynamic electricity price arbitrage. The results indicated that the RO approach can be beneficial for a non-conservative agent in the case of low daily price fluctuations, while, in summer, when the price fluctuations are higher, uncertainty can be ignored.

Optimal Participation of Residential Aggregators in Energy and Local Flexibility Markets

“…Robust models for home-level aggregation are still very scarce in the literature. Some proposals aim to minimize electricity bills [25], while others propose real-time decision making for batteries [26] or the management of thermal storage systems [27]. In contrast with [18]- [21], [23], [24], [28], and in line with [14]- [16], we propose a bidding strategy in both wholesale and local energy markets for flexibility offers, considering that Local Flexibility Markets (LFM) constitute an independent trading space/platform with specific bidding rules, following the recommendations of the literature [2], [5], [6].…”

Scheduling of grid‐tied battery energy storage system participating in frequency response services and energy arbitrage

“…In literature, there are also many papers relating to the energy arbitrage application [26][27][28][29][30][31]. Sioshansi et al [17] presented one of the leading studies on energy arbitrage that analysed four key aspects of the economic value of electricity storage in the Pennsylvania New Jersey Maryland (PJM) markets; the basic relationship among storage energy capacity, storage efficiency and the arbitrage value of energy storage; the accuracy of theoretical ES dispatch and the value of arbitrage using perfect foresight of future electricity prices; the temporal and regional variation in the value of energy arbitrage, investigating natural gas price variations, transmission constraints and fuel mixes on energy storage economics.…”

A Countermeasure for Preventing Flexibility Deficit under High-Level Penetration of Renewable Energies: A Robust Optimization Approach

“…Therefore, many studies on optimization techniques that can apply uncertainty have been carried out. In particular, in the power system industry, studies related to planning and operation have been considered based on an increase in uncertain resources such as renewable energy sources [19][20][21][22][23][24][25][26][27][28][29][30].…”

“…The authors in [22] proposed a novel stochastic planning framework to determine the optimal battery energy storage system (BESS) capacity and the year of installation in an isolated microgrid using a new representation of the BESS energy diagram. Studies on power system operation and planning using RO have been carried out to consider uncertainties such as renewable energy sources [23][24][25][26][27][28][29][30]. Ruiz and Conejo [23] presented a transmission expansion planning (TNEP) method by constructing the load and RES output into uncertainty sets.…”

“…The authors in [26] used variation in the net load as an uncertainty set, and proposed an economic dispatch to cope with variation in the use of ramp rates. In [27], the authors examined the effectiveness of RO in maximizing the economic benefit for owners of home battery storage systems in the presence of uncertainty in dynamic electricity prices. In [28], the authors proposed an adaptive robust optimization model for multiperiod economic dispatch, and introduced the concept of dynamic uncertainty sets and the methods to construct such sets for modeling the temporal and spatial correlations of uncertainty.…”