Mario Innorta

A stochastic model for the daily coordination of pumped storage hydro plants and wind power plants

We propose a stochastic model for the daily operation scheduling of a generation system including pumped storage hydro plants and wind power plants, where the uncertainty is represented by the hourly wind power production. In order to assess the value of the stochastic modeling, we discuss two case studies: in the former the scenario tree is built so as to include both low and high wind power production scenarios, in the latter the scenario tree is built on historical wind speed data covering a time span of one and a half year. The Value of the Stochastic Solution, computed by a modified new procedure, shows that in scenarios with low wind power production the stochastic solution allows the producer to obtain a profit which is greater than the one associated to the deterministic solution. In-sample stability of the optimal function values for increasing number of scenarios is reported.

A mixed integer nonlinear optimization model for gas sale company

In this paper the authors propose an optimisation model, called OMoGaS (Optimisation Modelling for Gas Seller), to assist companies dealing with gas retail commercialisation. The model takes into account the limits on price imposed by law on small consumers as well as the gas company policies in order to explore the commercial consequences of different policies. The GAMS framework is used for the optimisation of the defined MINLP model where the profit function is based on the number of contracts with the final consumers, on the tipology of consumers and on the cost supported to meet the final demand while the constraints include information on a maximum daily gas consumption, on yearly maximum and minimum comsumption in order to avoid penalties and on consumption profiles. A case study is presented.

A stochastic model for investments in different technologies for electricity production in the long period

We present a single stage stochastic mixed integer linear model for determining the optimal mix of different technologies for electricity generation, ranging from coal, nuclear and combined cycle gas turbine to hydroelectric, wind and photovoltaic, taking into account the existing plants, the cost of investment in new plants, maintenance costs, purchase and sale of

A GAS RETAIL STOCHASTIC OPTIMIZATION MODEL BY MEAN REVERTING TEMPERATURE SCENARIOS

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A Mixed Integer Linear Programming model of a zonal electricity market with a dominant producer

CO2 emission trading certificates and green certificates, in order to satisfy regulatory requirements. The power producer is assumed to be a price-taker. Stochasticity of future fuel prices, which affect the generation variable costs, is included in the model by means of a set of scenarios. The main contribution of the paper, beyond considering stochasticity in the future fuel prices, is the introduction of CVaR risk measure in the objective function in order to limit the possibility of low profits in bad scenarios with a fixed confidence level.

Cournot equilibria in oligopolistic electricity markets

We present deterministic and stochastic models for determining the optimal mix of different technologies for electricity generation, ranging from carbon, nuclear and combined cycle gas turbine to hydroelectric, wind and photovoltaic, taking into account the actual sites and the cost of investment in new sites, the cost of of mantainance, the use of emission quotas and the relative constraints as well as the green certificates one may use. The stochasticity is related to the future price of energy and to the future price of emissions, in this paper we limit our study to the variaility of fuels. The stochasticity appears in the expected costs and the probability that the total cost do not overcome a specific threshold is taken into account by considering CVaR risk measure. A comparison between the deterministic solution and the stochastic solution shows the role of using the risk the importance to use risk measure in the stochastic long run approach.

A stochastic optimization model for a gas sale company

The paper deals with a new stochastic optimization model, named OMoGaS-SV (Optimisation Modelling for Gas Seller-Stochastic Version), to assist companies dealing with gas retail commercialization. Stochasticity is due to the dependence of consumptions on temperature uncertainty. Due to nonlinearities present in the objective function, the model can be classified as an NLP mixed integer model, with the profit function depending on the number of contracts with the final consumers, the typology of such consumers and the cost supported to meet the final demand. Constraints related to a maximum daily gas consumption, to yearly maximum and minimum consumption in order to avoid penalties and to consumption profiles are included. The results obtained by the stochastic version give clear indication of the amount of losses that may appear in the gas seller’s budget.

A stochastic model for the single producer capacity expansion problem in the Italian electricity market

A stochastic programming model for the daily coordination of hydro power plants and wind power plants with pumped storage is introduced, with hourly wind power production uncertainty represented by means of a scenario tree. Historical data of wind power production forecast error are assumed to be available, which are used for obtaining wind power production forecast error scenarios. These scenarios are then combined with information from the weather forecast, resulting in wind power production scenarios. Ex-ante and ex-post measures are considered for assessing the value of the stochastic model: the ex-ante performance evaluation is based on the Modified Value of Stochastic Solution for multistage stochastic programming, introduced independently in Escudero (TOP 15(1):48–66, 2007) and Vespucci (Ann Oper Res 193:91–105, 2012); the ex-post performance evaluation is defined in terms of the Value of Stochastic Planning, introduced in Schutz (Int J Prod Econ, 2009), that makes use of the realized values of the stochastic parameter. Both measures indicate the advantage of using the stochastic approach.