Real-time electricity pricing for industrial customers: Survey and case studies in the United States

Nezamoddini, Nasim; Wang, Yong

doi:10.1016/j.apenergy.2017.03.102

Cited by 85 publications

(35 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[2][3][4] To address the challenges associated with the rising peak loads as well as the imbalanced energy demand and supply, demand-side management (DSM), which includes energy efficiency (EE) and demand response (DR) programs, has emerged. [5][6][7][8] EE programs aim to reduce energy consumption without decreasing production throughput or service quality, and DR aims to curtail or shift the load during peak hours in response to economic incentives. 9 Energy-aware (EA) manufacturing and energy-costaware (ECA) manufacturing correspond to EE and DR programs, respectively, in the industrial sector.…”

Section: Introductionmentioning

confidence: 99%

Energy-cost-aware flow shop scheduling considering intermittent renewables, energy storage, and real-time electricity pricing

Khalaf

Wang

2018

Int J Energy Res

Self Cite

View full text Add to dashboard Cite

Summary The industrial sector is one of the major energy consumers that contribute to global climate change. Demand response programs and on‐site renewable energy provide great opportunities for the industrial sector to both go green and lower production costs. In this paper, a 2‐stage stochastic flow shop scheduling problem is proposed to minimize the total electricity purchase cost. The energy demand of the designed manufacturing system is met by on‐site renewables, energy storage, as well as the supply from the power grid. The volatile price, such as day‐ahead and real‐time pricing, applies to the portion supplied by the power grid. The first stage of the formulated model determines optimal job schedules and minimizes day‐ahead purchase commitment cost that considers forecasted renewable generation. The volatility of the real‐time electricity price and the variability of renewable generation are considered in the second stage of the model to compensate for errors of the forecasted renewable supply; the model will also minimize the total cost of real‐time electricity supplied by the real‐time pricing market and maximize the total profit of renewable fed into the grid. Case study results show that cost savings because of on‐site renewables are significant. Seasonal cost saving differences are also observed. The cost saving in summer is higher than that in winter with solar and wind supply in the system. Although the battery system also contributes to the cost saving, its effect is not as significant as the renewables.

show abstract

Section: Introductionmentioning

confidence: 99%

Energy-cost-aware flow shop scheduling considering intermittent renewables, energy storage, and real-time electricity pricing

Khalaf

Wang

2018

Int J Energy Res

Self Cite

View full text Add to dashboard Cite

show abstract

“…The intent of such scheme is to motivate customers to modify their consumption pattern in order to level the utilities' load demand over time . On the basis of the principles of PBDR schemes, the customer's payment is calculated considering three well‐known tariff rates including “valley,” “off‐peak,” and “peak.” So far, different research works have studied PBDR programs such as TOU pricing, RTP, and CPP schemes, taking advantage of predetermined time period clusters to assess the benefits of DR programs. All of the above‐mentioned studies are conducted on the basis of predetermined time period clusters pertaining to load demand.…”

Section: Introductionmentioning

confidence: 99%

Optimal time period clustering of time‐of‐use schemes based on elastic loads' responsiveness

Samadi

Aghamohamadi

Mahmoudi

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary In time‐of‐use (TOU) schemes, most similar clusters to load demand can properly map the consumption pattern. However, the performance of such scheme is affected by the load responsiveness to the obtained clusters. This paper presents a time period clustering approach for TOU schemes to find the optimal clusters, using not only the load patterns but also its modified version obtained by responsiveness of elastic loads. A responsive load model is employed to simulate the load pattern modifications considering the hourly prices, load elasticities, and customers' benefit function. An imperialist competitive algorithm (ICA) has been designed to iteratively solve the problem. The proposed approach maximizes the load factors pertaining to the modified seasonal load patterns that are obtained on the basis of load responsiveness to the allocated clusters in each iteration of ICA. A comprehensive seasonal case study has been conducted to evaluate the performance of the proposed model. Results and comparisons show that the proposed clustering technique can significantly enhance the performance of the existing TOU schemes in terms of load factor improvement and peak reduction as it considers both load patterns and the actual load responsiveness to the obtained clusters.

show abstract

“…On the customer side, energy end-users do respond to the long-term electricity contract and price schemes offered by the utilities; however, they are usually insensitive and uncomfortable with respect to the highly dynamic or real-time pricing, due to the lack of competence to immediately respond to the price signal or little awareness of instantaneous opportunity [40,41]. However, electricity prices that describe marginal costs can vary substantially over time.…”

Section: Price Scheme and Demand Responsementioning

confidence: 99%

The Next-Generation U.S. Retail Electricity Market with Customers and Prosumers—A Bibliographical Survey

et al. 2017

View full text Add to dashboard Cite

Due to the rapidly-changing technologies in the power industry, many new references addressing the frameworks and business models of the next-generation retail electricity market are entering the research community. In particular, considering new customers with considerable demand response awareness and so-called prosumers with localized power generation based on distributed energy resources (DERs), the next-generation retail electricity market infrastructure will be a level playing field for local energy transactions, strategic pricing scheme design, new business model design and building an innovative energy ecosystem. Consequently, there is an urgent need to keep track of international experiences and activities taking place in the field of the market mechanism design problem at the distribution level. This paper provides a comprehensive survey of recent technology developments and aims to inspire awareness of the further deregulation of the electricity market, especially in areas close to customers. We mainly bring attention to the more than 90 articles published during the past five years. The collected literature has been divided into different sections to discuss different aspects of the next-generation retail electricity market under the deregulated power industry.

show abstract

Real-time electricity pricing for industrial customers: Survey and case studies in the United States

Cited by 85 publications

References 30 publications

Energy-cost-aware flow shop scheduling considering intermittent renewables, energy storage, and real-time electricity pricing

Energy-cost-aware flow shop scheduling considering intermittent renewables, energy storage, and real-time electricity pricing

Optimal time period clustering of time‐of‐use schemes based on elastic loads' responsiveness

The Next-Generation U.S. Retail Electricity Market with Customers and Prosumers—A Bibliographical Survey

Contact Info

Product

Resources

About