Demand Side Energy Management via Multiagent Coordination in Consumer Cooperatives

Abstract

A key challenge in creating a sustainable and energy-efficient society is to make consumer demand adaptive to the supply of energy, especially to the renewable supply. In this article, we propose a partially-centralized organization of consumers (or agents), namely, a consumer cooperative that purchases electricity from the market. In the cooperative, a central coordinator buys the electricity for the whole group. The technical challenge is that consumers make their own demand decisions, based on their private demand constraints and preferences, which they do not share with the coordinator or other agents. We propose a novel multiagent coordination algorithm, to shape the energy demand of the cooperative. To coordinate individual consumers under incomplete information, the coordinator determines virtual price signals that it sends to the consumers to induce them to shift their demands when required. We prove that this algorithm converges to the central optimal solution and minimizes the electric energy cost of the cooperative. Additionally, we present results on the time complexity of the iterative algorithm and its implications for agents' incentive compatibility. Furthermore, we perform simulations based on real world consumption data to (a) characterize the convergence properties of our algorithm and (b) understand the effect of differing demand characteristics of participants as well as of different price functions on the cost reduction. The results show that the convergence time scales linearly with the agent population size and length of the optimization horizon. Finally, we observe that as participants' flexibility of shifting their demands increases, cost reduction increases and that the cost reduction is not sensitive to variation in consumption patterns of the consumers.

On truthful auction mechanism for cloud resources allocation and consumption shifting with different time slots

“…Unlike their work, our method is mainly based on auction theory. Andreas Veit et al 8 presented a novel multiagent coordination algorithm by reducing peak power consumption, which effectively shapes the electricity consumption of the cooperative. Unfortunately, they did not check the truthfulness and the individual rationality of an agent to join the cooperative.…”

“…Seller's offer is defined by . In this paper, we mainly explore two type of price curve functions, which can be seen in Figure 2: The staircase price curve (i.e., marginal price), which is widely used in the fields of cloud resources allocation and Smart Grid 8 . The staircase price curve is a nondecreasing function of the total aggregated consumption quality of cloud resources, where the prices are different in each time slot and the price has a threshold structure.…”

“…To this end, there seem to be two main ways to solve this problem: 6‐8 one is shifting users' consumption for cloud resource from peak to trough, the other is balancing the consumption of cloud resources in different time periods. Considering the extensive application of auction theory in various fields, 9 especially in resource allocation, in this paper, we are motivated to design an effective auction mechanism (see Figure 1) which combines the above two ideas to achieve the goal of balancing users' demands for cloud resources during peak and nonpeak hours.…”

Autonomous trading in modern electricity markets

“…For example, as of the current date, residential TOU pricing schemes are proposed to take effect in California starting January 2019 [91]. Existing work on TOU tariffs either has not considered competitive retail markets or has used more abstract, smaller-scale simulations compared with Power TAC [130,127,10,14,118,1,107,131,19]. In Power TAC, to the best of our knowledge the first broker that used TOU tariffs was Mertacor13 [66] (see Section 8.5).…”

“…Agent-based systems were proposed for coordinating energy procurement by a customer-cooperative [118]. This research focused on the customers' perspective, rather than on the retail brokers' perspective which is the focus of this dissertation:…”