Co-optimization of energy market and ancillary services

Chand, Pramesh; Sugianto, Ly Fie

doi:10.1109/ipec.2005.207054

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…7) The minimum OR requirement will not be specified in this framework due to the reasons that this co-optimization model is aimed to observe the optimal energy and OR schedule with the expected cost of unserved energy and imposing such OR requirement may prevent an observation of true cost reflected by optimal OR schedule. 8 …”

Section: Assumptions and Limitationsmentioning

confidence: 99%

“…This ranges from market model designed to suit a particular electricity market to market model proposed to illustrate a specific method of better solution [1]- [9]. For instance, such co-optimization models can be different in terms of application, e.g., forward and balancing markets [8]- [9], pricing schemes [10]- [13], and etc. A simplified model only focused on co-optimization principle is still not abundant in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Co-optimization of energy and operating reserve in real-time electricity markets

Siriariyaporn

Robinson

2008

2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies

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Energy and reserve co-optimization is thought to be an efficient approach for allocating resource and revenue in electricity market operations whereas other schools of thought such as sequential ancillary-service auction, separation of energy and reserve markets, and forward reserve markets, become more questionable. In recent years, some electricity markets in North America, aim to deploy such technique so as to ensure their system integrity in selecting right resources for right products at the right time and the right price. This framework proposes a computational model for co-optimizing energy and operating reserve (OR) products in real-time operation via linear programming (LP).Security-constrained economic dispatch (SCED) is applied to problem formulation representing physical and functional requirements in the multi-interconnected control areas. Scarcity pricing which reflects a true cost of generation inadequacy is also deployed in pricing scheme in the means of determining expected cost of unserved energy. In this linear program, an economic agent such as independent system operator (ISO) or regional transmission organization (RTO) minimizes the total cost of real-time operation while obtaining the optimal schedules of energy and OR, including spinning reserve (SR) and non-spinning reserve (NR). The objective function of the LP model includes variable cost of generating units, availability offers for on-line and off-line units, and expected cost of unserved energy.The sets of constraints are composed of load balance constraints, resource limit constraints, transmission constraints, ramping constraints, and operating reserve constraints. The linear cooptimization model simplifies market design and implementation while not compromising any essential properties of electric power system. A validity of this framework is illustrated by a numerical example.Index Terms -power system economics, electricity market, securityconstrained economic dispatch, ancillary services, and power system planning and operation.

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Section: Assumptions and Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Co-optimization of energy and operating reserve in real-time electricity markets

Siriariyaporn

Robinson

2008

2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies

View full text Add to dashboard Cite

show abstract

“…These classical techniques use approximations and linearization procedures in order to limit complexity of the optimization problem. In [5] a heuristic based technique called horizon scan is used to find optimal solution for the problem of economic dispatch with linear and nonlinear constraints given by the dynamics of power systems. Pareto-based evolution strategy was applied in [6] to minimize the costs for providing reserve aid for covering transmission losses.…”

Section: Introductionmentioning

confidence: 99%