Personalized real time pricing for efficient and fair demand response in energy cooperatives and highly competitive flexibility markets

Abstract

This paper contributes to the well-known challenge of active user participation in demand side management (DSM). In DSM, there is a need for modern pricing mechanisms that will be able to effectively incentivize selfishly behaving users in modifying their energy consumption pattern towards system-level goals like energy efficiency. Three generally desired properties of DSM algorithms are: user satisfaction, energy cost minimization and fairness. In this paper, a personalized-real time pricing (P-RTP) mechanism design framework is proposed that fairly allocates the energy cost reduction only to the users that provoke it. Thus, the proposed mechanism achieves significant reduction of the energy cost without sacrificing at all the welfare (user satisfaction) of electricity consumers. The business model that the proposed mechanism envisages is highly competitive flexibility market environments as well as energy cooperatives.

A pricing scheme for electric utility's participation in day-ahead and real-time flexibility energy markets

“…In order to evaluate the proposed system, we use the following key performance indicators (KPIs), also widely accepted in similar studies [22,29]. 1) ESP profit according to (10) which is the sum of consumers' bills (retail market) plus the revenues from flexibility market minus the cost of energy in the whole sale market. 2) W is given according to (11), and intuitively expresses the competitiveness of an ESP that adopts a billing strategy in an open retail electricity market.…”

“…In addition to these bills, in case that ESP wants to realize more profits, it can multiply each bill with a factor 1 þ p. Thus, the previous equation becomes: According to (10), in the case that an ESP selects the values of ðp; L ESP Þ, Algorithm 1 calculates the energy consumption (x k i ) and the bill (B k i ) of each user i at k. According to these (at time instant k), the profits of the ESP, noted as P k , are given from (10) and the W is given from (11).…”

“…Indicatively, Bayesian optimization [32] can be also used for more general functions (in cases that user model is more general and/or unknown and differs from the one that is presented in Section 4.1, but in this case the proposed methodology may suffer from slow convergence). Alternatively convex optimization [31] can be used if the two above-mentioned use cases and user model are assumed, which leads to a convex form of the P k in (10) and can converge faster. For the methodology with which the user model parameters are derived, we refer to [17,26,27].…”

“…In the context of SOCIALENERGY [6,7], we focus on the development of innovative business models for ESPs and their automated and optimal operation through an ICT platform [8]. In our previous work, we exploit communities of end users and their social interaction [9,10] in order to trigger behavioral changes in energy consumption. Incentivized from all the above and by evolving the logic and the architecture of the recent pricing schemes found in the literature, this paper presents: 1) A novel pricing scheme architecture, referred to as flexibility real-time pricing (FRTP), which allows progressive ESPs to simultaneously participate in multiple energy markets e.g.…”

Transactive Energy for Flexible Prosumers Using Algorithmic Game Theory

“…With respect to the functions s n (•), their choice has been subject to discussion in recent literature on energy communities (e.g. [14], [32], [28]) and relates to how the costs of energy are shared among the community members. Nevertheless, it is easy for the community manager to make sure that the payments received add up to the energy costs, i.e., Algorithm 1 Combinatorial auction for transactive energy 1: Initialize b n for every bidder n 2: Initialize x t n = x max n , ∀t for every bidder n 3: Initialize k = 1…”