WP1-4 Southern European district heating

Supervisor(s)

Hosted by:

Sampol Ingeniería y Obras

Student:

Nicolás Perez de la Mora

Started: 

09/01/2014

Supervisor(s):

Prof. Victor Martínez Moll (UIB)

Dr. Vicenç Canals (UIB)

Short description

The PhD focuses on developing a decision maker tool that allows the plant manager to operate optimizing the energy generation and allowing solar thermal to provide a high solar fraction.

Forecasting tools are included in a set of appropriated computer tools which work together with thermal simulation optimizer based on MatLab in order to estimate the energy generation strategies by determining the energy production mix that minimizes the energy cost and optimize the electricity production. This leads to an optimization of the plant operation and integration of the solar field generation.

Parc Bit is a power plant comprising a Combined Heat and Power (CHP), biomass boiler, diesel boiler, solar collectors and electrical and absorption chillers. Therefore the plant is able to generate both thermal and electrical energies and thus able to obtain revenue injecting electricity into the grid and supplying thermal energy as heating and cooling. In order to maximize the plant's revenue for energy generation it is needed to fit generation and demand curves in the best possible way.

To achieve a proper energy curve fitting is needed, besides detailed generation knowledge of the power plant, an accurate estimation of thermal energy consumption and the electricity pool price. Creating a plant simulator to include the forecasted information into the model attempts helping the plant's manager to improve the generation strategies through reducing expenses and maximizing revenues.

Once this model is functional and including the forecasted information a decision maker tool is developed based on Mixed Integer Linear Programming (MILP). This tool allows the plant manager to operate according with the acquired information and thus, optimizing the energy generation.

Project background (click to open)

In the Southern European countries solar thermal energy is harnessed few moths per year as heating, therefore solar cooling is a suitable option to increase the yield of thermo solar fields that support tri-generation power plants. In this kind of facilities solar heating and cooling integration, hardly increases the complexity of the district network management strategies. This strategies depends on the decision maker system which aims to adjust the thermal (including solar) and electric tri-generation production curves to the forecasted consumption lowering the costs and maximizing the benefits.

Methodology (click to open)

In order to optimize the plant generation several inputs are required: heating and cooling demand, solar thermal generation, electric energy price as much as the behaviour of the generation systems and district network and its heat losses under different operating conditions.

A two cores forecasting tool are developed based on ARIMA (Auto Regressive Integrated Moving Average) and ANN (Artificial Neural Networks) methods in order to obtain forecasts of: heating and cooling demand, solar thermal generation and electric energy price. The used methods are:

  • ARIMAX: Auto Regressive Integrated Moving Average with Explanatory variable.
  • NARX: Nonlinear AutoRegressive models with eXogenous Neural Network.

To obtain information about the generation systems and district network real data is acquired from the plant and validates the model to develop in MatLab. Once this model is functional and including the forecasted information a decision maker tool is developed based on MILP. This tool allows the plant manager to operate the plant according with the acquired information and thus, optimizing the energy generation and allowing solar thermal to provide a higher solar fraction.

Numerical tools:

The two cores forecasting tool are developed using MatLab™. Simulation model is based on MatLab™. Decision making tool is developed using MatLab™.

Working plan and current status (click to open)

Achieved milestones:

  • Literature review (M3).
  • Definition of research method (M3).
  • Zero-version plant model in TRNSYS (M4).
  • Acquisition of electricity market data (M6).
  • Develop a tool for electricity market forecasting (M7).
  • Evaluation of Electricity Price Forecaster results (M9).
  • Acquisition and validation of demand data (M6).
  • Acquire district network losses (M6).
  • Develop a Heating/Cooling demand Forecaster (M7).
  • Dissemination about Demand Forecasting and district network losses (M8).
  • Evaluation of Demand Forecaster results (M9).
  • Acquisition of Solar Generation data (M6).
  • Develop a tool for Solar Generation forecaster (M7).
  • Installation of data-registers in generation units (M6).
  • International Work Conference on Artificial Neural Networks (M8).
  • Plant model validation with real data (M7).
  • Development of plant model and decision maker tool in MatLab™ (M9).

Milestones to achieve:

  • Review Article in Solar District Heating and Cooling with WP1 (M8).
  • Electricity Price Forecaster dissemination (M8).
  • Plant model optimization dissemination (M8).
  • Evaluation of Solar Generation results (M9).