Implementing flexible demand: Real-time price vs. market integration

Abstract

This paper proposes an agent-based model that combines both spot and balancing electricity markets. From this model, we develop a multi-agent simulation to study the integration of the consumers' flexibility into the system. Our study identifies the conditions that real-time prices may lead to higher electricity costs, which in turn contradicts the usual claim that such a pricing scheme reduces cost.We show that such undesirable behavior is in fact systemic. Due to the existing structure of the wholesale market, the predicted demand that is used in the formation of the price is never realized since the flexible users will change their demand according to such established price.As the demand is never correctly predicted, the volume traded through the balancing markets increases, leading to higher overall costs. In this case, the system can sustain, and even benefit from, a small number of flexible users, but this solution can never upscale without increasing the total costs.To avoid this problem, we implement the so-called "exclusive groups". Our results illustrate the importance of rethinking the current practices so that flexibility can be successfully integrated considering scenarios with and without intermittent renewable sources. else if dif f 60(t) < −LIM IT then 43: priceH[t] ← min(price[t · 4 : t · 4 + 4]) 44: end if 45: end for 46: return priceH 47: end procedure

Energy Internet via Packetized Management: Enabling Technologies and Deployment Challenges

“…However, a transition from existing liberalized markets to this approach can be done via virtual micro-grids that treat their energy according to principles described here while participating in the wholesale market (probably together with other micro-grids). This approach is also aligned with the solutions proposed in [37], [61], where the P2P aggregators would play a bigger role by creating possible local energy interchanges that would lead to the upcoming Energy Internet. Overall, the proposed Energy Internet can only emerge if the following open research topics are tackled: ‚ Packetized Energy Management: It is necessary to research the best ways to discretize dispatchable loads to be handled by an energy server, whose management algorithm can solve (large) combinatorial problems in few minutes.…”

“…It is interesting to note that, in European electricity markets, there exist the so-called Exclusive Group (e.g. [37]) where daily profiles are bid, not independent hour-by-hour bids. In this class of bidding, the load profile selected by the market matching algorithm shall be realized.…”

“…These results evince the existence of systemic emergent phenomena that are undesirable when operating power grids, mainly due to a combination between lack of coordination caused by poorly designed information signals. The authors provide possible solutions to the issue, including direct signals for micro-grids [61] and exclusive block bids in wholesale market [37].…”

Why Smart Appliances May Result in a Stupid Grid: Examining the Layers of the Sociotechnical Systems

“…Price is a metric that reflects (at least in theory) the willingness or need of buying by the consumers and availability of supply. In the electricity wholesale markets, it indicates the most expensive power produced to match the demand in a given period of time [14], [15]. It is worth noting that price and frequency are associated to different timescales.…”

A Cyber-Physical Residential Energy Management System via Virtualized Packets

“…In this sense, our solution is computationally light while effective in peak-shaving. Although simple, the proposed packetized management could be useful in supporting the market integration of demand-response based on Exclusive Group bids [41] where retailers or aggregators bid daily profile curves in the day-ahead market that should be realized. Note, however, that direct allocation using packetized energy makes any quantitative comparison with existing distributed (price-based, or incentive-based) or centralized (globally optimizer) allocation unfair-even unfeasible-since the scenarios used to analyze the algorithm performance are built upon quite a different set of assumptions and methodological choices.…”