Real-Time Scheduling of Demand Response Options Considering the Volatility of Wind Power Generation

Optimal Allocation Method of Residential Air-Conditioners: Trade-Off Solutions Between Economic Costs and Aggregation Reliability

“…Using the reward and punishment mechanism, such measures cannot fundamentally always meet the given reliability requirements. Reference [20] and [21] propose to approximate the uncertainty of residents' responses through stochastic optimization models. However, the probability distribution of uncertain variables is difficult to characterize, and the economics of the allocation plan has not been properly considered.…”

Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing

“…Demand response (DR) is designed to change electricity consumption patterns, which includes shifting electricity load from on-peak to off-peak periods, shifting electricity consumption to when renewable energy is abundant, reducing demand when the system reliability is jeopardized, or responding to dynamic price signals. DR can be applied to (1) reduce greenhouse gas (GHG) emissions by integrating renewable energy [1,2], (2) provide ancillary services such as frequency control and operating reserves [3,4], and (3) balance power generation and demand in an electricity market [2,5,6]. DR is considered as the most economical approach to these applications and has gained increasing attention from both academics and industry.…”

“…Compared to DR implementation in the commercial/industry sector, residential DR implementation is more challenging because: (1) individual residential load is small in scale; (2) residential electricity models were developed for applications in the electricity market [2,5,6,30]. A distributed random access framework was developed to mitigate bus congestion and voltage drops [31].…”

Layered stochastic approach for residential demand response based on real‐time pricing and incentive mechanism

“…Demand response (DR), on the other hand, is designed to enable demand controllability by changing end‐user's electricity consumption patterns, which includes (i) shifting electricity load from on‐peak to off‐peak periods; (ii) shifting consumption based on renewable energy availability; (iii) responding to price signals in an electricity market; (iv) responding to control signals when the power system is jeopardised. Accordingly, a DR implementation can provide the following benefits: (i) savings of significant capital cost to be used for building new power plants by reducing peak demand; (ii) greenhouse gas emission reduction by integrating renewable energy [1, 2]; (iii) cost‐effective balance of power generation and demand in an electricity market [2–4]; (iv) economical ancillary services (ASs) such as frequency control and operating reserves [5–7]. DR is considered as one of the most economical methods to achieve these benefits and this has brought increasing attention from both academia and industries.…”

“…The impacts of human behaviour on a DR application have been investigated in [11]. A two‐stage stochastic programming model was developed to incorporate wind power into an electricity market [2]. The work in [12] also proposed a two‐stage stochastic programming model to evaluate random electricity consumption.…”