The paper provides a formulation of the generation and transmission expansion problem which incorporates resource adequacy assessment into the stochastic optimization framework. The system expansion problem is conside...
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(纸本)9781467345378
The paper provides a formulation of the generation and transmission expansion problem which incorporates resource adequacy assessment into the stochastic optimization framework. The system expansion problem is considered as an auction resolved through a stochasticmixedinteger linear problem in which generation and transmission expansion offers are explicitly identified on the electrical grid modeled using linearized DC SCOPF. In this formulation, locational resource adequacy indicators are derived as dual variables: shadow prices for reliability limiting transmission facilities and Locational Reliability Prices. The paper demonstrates that the auction can identify an optimal mix of generation and transmission expansion offers which guarantees resource adequacy of the system at least costs. Moreover, the problem of transmission cost allocation is resolved through the market pricing mechanism. The paper extends the results of [1] by incorporating transmission expansion options. A novel technique of modeling flexible transmission topology using flow-cancelling transactions [2] is applied.
This paper presents a stochastic model for the optimal risk-based generation maintenance outage scheduling based on hourly price-based unit commitment in a generation company (GENCO). Such maintenance outage schedules...
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This paper presents a stochastic model for the optimal risk-based generation maintenance outage scheduling based on hourly price-based unit commitment in a generation company (GENCO). Such maintenance outage schedules will be submitted by GENCOs to the ISO for approval before implementation. The objective of a GENCO is to consider financial risks when scheduling its midterm maintenance outages. The GENCO also coordinates its proposed outage scheduling with short-term unit commitment for maximizing payoffs. The proposed model is a stochasticmixedinteger linear program in which random hourly prices of energy, ancillary services, and fuel are modeled as scenarios in the Monte Carlo method. Financial risks associated with price uncertainty are considered by applying expected downside risks which are incorporated explicitly as constraints. This paper shows that GENCOs could decrease financial risks by adjusting expected payoffs. Illustrative examples show the calculation of GENCO's midterm generation maintenance schedule, risk level, hourly unit commitment, and hourly dispatch for bidding into energy and ancillary services markets.
Combinatorial optimization problems have applications in a variety of sciences and engineering. In the presence of data uncertainty, these problems lead to stochastic combinatorial optimization problems which result i...
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Combinatorial optimization problems have applications in a variety of sciences and engineering. In the presence of data uncertainty, these problems lead to stochastic combinatorial optimization problems which result in very large scale combinatorial optimization problems. In this paper, we report on the solution of some of the largest stochastic combinatorial optimization problems consisting of over a million binary variables. While the methodology is quite general, the specific application with which we conduct our experiments arises in stochastic server location problems. The main observation is that stochastic combinatorial optimization problems are comprised of loosely coupled subsystems. By taking advantage of the loosely coupled structure, we show that decomposition-coordination methods provide highly effective algorithms, and surpass the scalability of even the most efficiently implemented backtracking search algorithms.
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