Demand side management in a day-ahead wholesale market: A comparison of industrial &amp; social welfare approaches

Jiang, Bo; Farid, Amro M.

doi:10.1016/j.apenergy.2015.07.014

Cited by 29 publications

(14 citation statements)

References 72 publications

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“…Measures are being implemented in the power sector to alleviate this air pollution problem. On one hand, the overall emission characteristics of thermal units 5 have been greatly improved. On the other hand, the capacity of renewable energy, such as wind and solar power, has grown rapidly in the last decade.…”

Section: Motivation and Aimmentioning

confidence: 99%

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Electricity production scheduling under uncertainty: Max social welfare vs. min emission vs. max renewable production

et al. 2017

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Some areas in China are facing pressing air pollution problems. Measures from the power sector can be taken to cope with air pollution issues, including reducing emission levels of thermal units and integrating wind and solar power. Social welfare, emission, and renewable integration are three major concerns in modern power system operations. This paper describes three stochastic scheduling models aiming at maximizing social welfare (SW), minimizing emission (EM) and maximizing renewable production (RE).A multi-objective scheduling model (MT) is also proposed that properly balances the above objectives.Wind power uncertainty and dispatchable loads are considered in the model. The outcomes of the three models are compared through an illustrative example and a 57-node case study. Results show that model EM results in 36% of the social welfare of model SW, 27% of its emissions, and 43% of its wind spillage, while model RE results in 55% of the social welfare of model SW, 56% of its emissions and 28% of its wind spillage. Additionally, we analyze how the optimal generation scheduling is affected by the weights in model MT. This work provides insight to policy makers on how to balance social welfare, emissions and renewable production. teristics of wind power increase the possibility of energy curtailment. Wind spillage reached 15% of wind 10 production in China in 2015 [2].Besides deployment of up/down reserves by thermal units, dispatchable loads provide an appropriate mechanism to deal with uncertainty. Dispatchable loads can be scheduled and re-dispatched like generators to achieve the scheduling objective. Dispatchable loads increase the flexibility of the system, which is helpful in system with high renewable production. 15To minimize the operating cost is the main objective for most Independent System Operators (ISO), but nowadays other objectives appear. For some highly-polluted areas, the ISO may seek to minimize emissions to ensure air quality. For areas with high renewable integration, the ISO may seek to maximize renewable production to make electricity production as "green" as possible. These distinct objectives result in different generation scheduling outcomes. 20Considering the pressing problems of air pollution and wind curtailment in China, this paper focuses on the comparison of three stochastic scheduling models taking into account wind power uncertainty and dispatchable loads. The objectives are to maximize social welfare, to minimize emissions, and to maximize renewable integration, respectively. A multi-objective scheduling model that properly combines the above objectives is proposed as well. A similar methodology has not been reported in the technical literature. 25An illustrative example and a 57-node case study are considered to illustrate the proposed models. Literature ReviewRelevant works pertaining to electricity generation scheduling models with different objectives are reviewed below. Minimizing cost (or maximizing social welfare) is the most common goal [3,4] in unit commitment proble...

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Section: Motivation and Aimmentioning

confidence: 99%

“…Cost for generator start-up and operation and the utility of demand are considered in objective (1). The social welfare is defined as the total utility of loads minus the total cost of generation and the cost of load shedding [5,31,32]. f 1 in Equation (1) is the minus social welfare to be minimized.…”

Section: Objectivesmentioning

confidence: 99%

Electricity production scheduling under uncertainty: Max social welfare vs. min emission vs. max renewable production

et al. 2017

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“…Examples of previous work include [25] developed an agent based model to optimise price bidding for energy market traders, generating significant cost savings for participants. [26] compared the effectiveness for trading electricity from the dayahead electricity wholesale market using the social welfare with the industrial load reduction models.…”

Section: Demand Side Management Strategiesmentioning

confidence: 99%

Assessing energy business cases implemented in the North Sea Region and strategy recommendations

Nayak

Gray

et al. 2016

Applied Energy

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“…For day-ahead scheduling in this paper, we assign load curtailment to real-time market [23] and concentrate on load shifting. That means the total demand for each consumer in the next day is a constant in this scheduling and we have…”

Section: Proposed Optimal Day-ahead Scheduling Problemmentioning

confidence: 99%

Distributed Optimal Day-Ahead Scheduling in a Smart Grid: A Trade-Off among Consumers, Power Suppliers, and Transmission Owners

Xiong

Liu

2017

Complexity

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To cope with the challenges due to increasing peak load, an optimal day-ahead scheduling problem for social welfare maximization is proposed, in which not only the comfort level of consumers and costs of power suppliers but also the power losses in transmission and operation costs of transmission owners are taken into account. Then this optimal day-ahead scheduling problem is reformulated and solved via the alternating direction method of multipliers (ADMM), by which fast convergence is guaranteed and the privacy of participants is ensured, in a distributed manner. Specifically, in the proposed distributed optimal day-ahead scheduling, the hourly prices for consumers are divided into hourly supply prices and hourly delivery prices, which will be updated by the independent system operator based on the hourly demand-supply situations and hourly demand-delivery situations, respectively. And the consumers, power suppliers, and transmission owners make their individual optimal day-ahead scheduling based on their individual hourly prices, hourly supply prices, and hourly delivery prices, respectively, until the hourly demand-supply balances and hourly demand-delivery balances are achieved. Effectiveness of the proposed distributed optimal day-ahead scheduling is verified by the cases studied.

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Demand side management in a day-ahead wholesale market: A comparison of industrial & social welfare approaches

Cited by 29 publications

References 72 publications

Electricity production scheduling under uncertainty: Max social welfare vs. min emission vs. max renewable production

Electricity production scheduling under uncertainty: Max social welfare vs. min emission vs. max renewable production

Assessing energy business cases implemented in the North Sea Region and strategy recommendations

Distributed Optimal Day-Ahead Scheduling in a Smart Grid: A Trade-Off among Consumers, Power Suppliers, and Transmission Owners

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