Impact of demand response on thermal generation investment with high wind penetration

Abstract

We present a stochastic programming model for investments in thermal generation capacity to study the impact of demand response (DR) at high wind penetration levels. The investment model combines continuous operational constraints and wind scenarios to represent the implications of wind variability and uncertainty at the operational level. DR is represented in terms of linear price-responsive demand functions. A numerical case study based on load and wind profiles of Illinois is constructed with 20 candidate generating units of various types. Numerical results show the impact of DR on both investment and operational decisions. We also propose a model in which DR provides operating reserves and discuss its impact on lowering the total capacity needed in the system. We observe that a relatively small amount of DR capacity is sufficient to enhance the system reliability. When compared to the case with no DR, a modest level of DR results in less wind curtailment and better satisfaction of reserve requirements, as well as improvements in both the social surplus and generator utilization, as measured by capacity factors. The authors acknowledge the U.S. Department of Energy's Wind Power Program, for funding the research presented in this paper. The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (''Argonne''). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up non-exclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.Shan

System state model based multi‐period robust generation, transmission, and demand side resource co‐optimisation planning

“…After x is revealed, the most economically inefficient scenarios under uncertainties are detected in the second stage and fed back to the first stage for adjusting planning decisions, where y and z, respectively, represent second-stage binary and continuous variables. The objective (15) is to minimise the total cost, including investment cost (16) of candidate assets and total operation cost (17) of multiple years within the planning horizon. Investment cost (16) contains construction costs of new generators and lines and deployment costs of new DSR programs.…”

“…A survey on state-ofthe-art DR applications in utilities and effective approaches to incorporate DRs into long-term planning was summarised in [16]. A two-stage stochastic GEP model was presented in [17] which formulates DRs in the form of price-sensitive energy resources or operating reserves. Kazerooni and Mutale [18] proposed a robust TEP model, in which incentives of price-responsive DRs are derived from usage and reliability charges.…”

“…From existing literature we notice that (i) prior work on quantifying impacts of DSR programs on long-term GTEP is rather limited [17][18][19], especially in terms of how coordinated long-term planning of DSRs could impact expensive generation/transmission asset investments in the planning stage and improve economic efficiency in the operation stage; (ii) most existing DSR integrated long-term resource planning works in industry practice are utilityoriented [16], which are mostly implemented by utility company, aiming at maximising the total profit. However, as market information is usually incomplete to market participants, DSR participation decisions via utility-oriented programs might not be the best choice for the whole system.…”

Cooperative Planning of Renewable Generations for Interconnected Microgrids

“…Yang and Nehorai [5] studied a planning problem for energy storage and generators in a microgrid, and formulated a joint optimization problem to minimize the total investment and operational cost. Jin et al [6] studied the impact of demand response on the thermal generation investment. The studies in [2]- [6] all focused on capacity investment problems from a single microgrid operator or planner's perspective.…”

“…Jin et al [6] studied the impact of demand response on the thermal generation investment. The studies in [2]- [6] all focused on capacity investment problems from a single microgrid operator or planner's perspective. Renewable energy generations and load profiles vary in different geographical locations and at different time periods of a day.…”