The large-scale penetration of renewable energy, such as wind power, brings a lot of economic and environmental benefits to the grid, and it also causes hidden dangers in the reliability and security of the power system due to its uncertainty. As an effective demand-side management method, demand response has unique advantages in smoothing wind power fluctuations and mitigating grid pressure. This paper proposes a new model for the demand response aggregator (DRA) that includes both combined heat and power systems (CHPS) and energy storage devices. DRA can interact with the Independent system operator (ISO) through combined heat and power (CHP) units, energy storage devices, and the heat buffer tank to benefit from the electricity market and the thermal market simultaneously. At the same time, wind power producers (WPP) are modeled to turn wind power that was initially passively consumed into active market participants. The problem is modeled using an improved weighted method, which aims to take the diverse objectives of multiple market participants into account. The proposed model is tested on the modified IEEE RTS-24 test system to analyze the optimal scheduling strategies of each participant in the power market. INDEX TERMS Combined heat and power, demand response, electricity market, electricity storage, wind power producer. NOMENCLATURE Most of the symbols and notations used throughout this paper are defined below for quick reference. Others are defined following their first appearances, as needed.
Study Sponsors and Key PlayersThe CAISO/PLEXOS 33% Renewable Portfolio Standard (RPS) study was initiated by the California Public Utilities commission (CPUC) in late 2008. The CPUC tasked the California Independent Systems Operator (CAISO) to manage and perform the study. In turn, CAISO partnered with a team at PLEXOS Solutions (a software vendor) to build the model, and tasked analysts at Southern California Edison (SCE) to run the model. The SCE analysts also worked directly with PLEXOS Solutions to refine and reconfigure the model as appropriate over the course of the study.Lawrence Livermore National Laboratory (LLNL) was contacted by the CPUC after the analysts at SCE observed very slow runtimes in the model, necessitating a shift in the study completion date from April 2010 to February 2011. Specifically, a single month of the model was taking over a day to run, and a yearly calculation thus took several days. The CPUC asked whether LLNL high-performance computing (HPC) resources could be used to speed execution time of the model, and a team at LLNL investigated. An initial feasibility study was performed by LLNL in March-April 2010, followed by a comprehensive effort starting in August 2010 and continuing through mid-2011.
Objectives and Phases of the CAISO/PLEXOS 33% RPS StudyThe study's official title is "ISO Study of Operational Requirements and Market Impacts at 33% Renewable Portfolio Standard (RPS)." The stated objectives are twofold: 1) identifying operational requirements and resource options to reliably operate the ISO-controlled grid under a 33% RPS in 2020; and 2) inform market, planning, and policy/regulatory decisions by the ISO, state agencies, market participants, and other stakeholders.The first of these objectives requires the hourly estimates of integration requirements, measured in terms of operational ramp, load following and regulation capacity and ramp rates, as well as additional capacity to resolve operational violations. It also involves consideration of other variables that affect the results, such as the impact of different mixes of renewable technologies, and the impact of forecasting error and variability. The second objective entails supporting the CPUC to identify longterm procurement planning needs, costs, and options, as well as informing other decisions made by the CPUC and state agencies. For the ISO itself this includes informing state-wide transmission planning needs for renewables up to a 33% RPS, and informing design of wholesale markets for energy and ancillary services to facilitate provision of integration capacities.
CPUCCAISO PLEXOS SCE LLNL
Builds Model Runs Model Initiates StudyManages Study Improves Runtimes
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