Generation asset planning under uncertainty.

With the introduction of competition in the electric power industry, generation asset planning must change. In this changed environment, energy companies must be able to capture the extrinsic value of their asset operations and long-term managerial flexibility for sound planning decisions. This dissertation presents a new formulation for the generation asset planning problem under market uncertainty, in which short-term operational and long-term coupling constraints associated with investment decisions are simultaneously reflected in the planning process

Entanglement transmission and generation under channel uncertainty: Universal quantum channel coding

We determine the optimal rates of universal quantum codes for entanglement transmission and generation under channel uncertainty. In the simplest scenario the sender and receiver are provided merely with the information that the channel they use belongs to a given set of channels, so that they are forced to use quantum codes that are reliable for the whole set of channels. This is precisely the quantum analog of the compound channel coding problem. We determine the entanglement transmission and entanglement-generating capacities of compound quantum channels and show that they are equal. Moreover, we investigate two variants of that basic scenario, namely the cases of informed decoder or informed encoder, and derive corresponding capacity results.Comment: 45 pages, no figures. Section 6.2 rewritten due to an error in equation (72) of the old version. Added table of contents, added section 'Conclusions and further remarks'. Accepted for publication in 'Communications in Mathematical Physics

A Multiperiod OPF Model Under Renewable Generation Uncertainty and Demand Side Flexibility

Renewable energy sources such as wind and solar have received much attention in recent years and large amount of renewable generation is being integrated to the electricity networks. A fundamental challenge in power system operation is to handle the intermittent nature of the renewable generation. In this paper we present a stochastic programming approach to solve a multiperiod optimal power flow problem under renewable generation uncertainty. The proposed approach consists of two stages. In the first stage operating points for conventional power plants are determined. Second stage realizes the generation from renewable resources and optimally accommodates it by relying on demand-side flexibility. The benefits from its application are demonstrated and discussed on a 4-bus and a 39-bus systems. Numerical results show that with limited flexibility on the demand-side substantial benefits in terms of potential additional re-dispatch costs can be achieved. The scaling properties of the approach are finally analysed based on standard IEEE test cases upto 300 buses, allowing to underlined its computational efficiency.Comment: 8 pages, 10 figure

Long Term Power Generation Planning Under Uncertainty

Generation expansion planning concerns investment and operation decisions for different types of power plants over a multi-decade horizon under various uncertainties. The goal of this research is to improve decision-making under various long term uncertainties and assure a robust generation expansion plan with low cost and risk over all possible future scenarios. In a multi-year numerical case study, we present a procedure to deal with the long term uncertainties by first modeling them as a multidimensional stochastic process and then generating a scenario tree accordingly. Two-stage stochastic programming is applied to minimize the total expected cost, and robust optimization is further applied to reduce the cost variance. Results of experiments on a realistic case study are compared. An efficient frontier of the planning solutions that illustrates the tradeoff between the cost and risk is further shown and analyzed