This paper presents a genetic algorithm (GA) solution to the unit commitment problem. GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. A simple GA algorithm implementation using the standard crossover and mutation operators could locate near optimal solutions but in most cases failed to converge to the optimal solution. However, using the varying quality function technique and adding problem specific operators, satisfactory solutions to the unit commitment problem were obtained. Test results for power systems of up to 100 units and comparisons with results obtained using Lagrangian relaxation and dynamic programming are also reported.

A genetic algorithm solution to the unit commitment problem

Unit commitment is a complex problem which, until now and for real size systems, has been solved using heuristic methods only. This paper proposes a new Lagragian relaxation method, which produces ??-optimal solutions. The flexibility of the algorithm is such that numerous developments can be envisaged, such as simultaneous management of pumping units, probabilistic determination of the spinning reserve, etc.

A New Method for Unit Commitment at Electricite De France

We estimate the long-run economic value of photovoltaic (PV) generation with increasing penetration using a unique investment and dispatch model that captures long-run investment decisions while also incorporating detailed operational constraints and hourly time resolution over a full year. High time resolution and operational constraints can be important for estimating the economic value of variable generation resources like PV, as is the use of a modeling framework that accommodates new investment decisions. The model is herein applied to a case study that is loosely based on California in 2030. The marginal economic value of PV is decomposed into capacity value, energy value, day-ahead forecast error cost, and ancillary services. The value of PV is found to exceed the value of a flat block of power by $19/MWh at low penetration, largely due to the high capacity value of PV at low penetration. The value of PV is found to drop considerably (by more than $60/MWh) as the penetration increases toward 30% on an energy basis, first due primarily to a steep drop in the capacity value followed by a decrease in the energy value. Day-ahead forecast error and ancillary service costs, though not insignificant, do not change as dramatically with increasing penetration. In the near term, efforts to mitigate changes in the value of PV with increasing penetration may be most effective if focused on maintaining the capacity value of PV.

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Changes in the Economic Value of Photovoltaic Generation at High Penetration Levels: A Pilot Case Study of California

LBNL-XXXX E RNEST O RLANDO L AWRENCE B ERKELEY N ATIONAL L ABORATORY Changes in the Economic Value of Variable Generation at High Penetration Levels: A Pilot Case Study of California Andrew D. Mills and Ryan Wiser Environmental Energy Technologies Division March 2013 18th Annual POWER Conference on Energy Research and Policy. Energy Institute at Haas: Berkeley, CA. March 22, 2013 The work described in this paper was funded by the Office of Electricity Delivery and Energy Reliability (Research & Development Division and National Electricity Delivery Division Division) and by the Office of Energy Efficiency and Renewable Energy (Wind and Hydropower Technologies Program and Solar Energy Technologies Program) of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

https://escholarship.org/content/qt8h4611jp/qt8h4611jp.pdf

G. Côté

Papers

Decomposition techniques in power system planning: the Benders partitioning method

Prediction of reserve requirements in generation planning