In the southern European countries, solar thermal energy is harnessed a few months per year for heating. Therefore, solar cooling is a suitable option to increase the yield of thermo-solar fields that support tri-generation power plants. In these kinds of facilities, solar heating, and cooling integration does not significantly increase the complexity of the district network management strategies. These strategies depend on the decision-making system which aims to adjust the thermal and electric production curves to the forecasted consumption to lower the costs and maximise the benefits.
Energy generation is tied to energy consumption. Thus, the lack of infor-mation about the load or energy generation costs may lead to sub-optimally managed networks and energy waste. To achieve a fitting of energy between the load and generation, information is needed about the power plant as well as the district network and its distribution cost, primary energy cost, energy consumption estimation, and accurate energy price forecast.
Creating an energy management system (EMS) and including forecasted information into the model aims to assist the power plant manager to improve the generation strategies, maximise benefits by reducing production costs, and optimise the energy distribution through the DHC network.
SAMPOL aims to advance its analytic maturity with the help of this PhD project. A four-stages data analytic maturity is proposed in . In Figure 1.3 an adaptation from the model is depicted. Thus, the aim of the project is to develop an EMS tool that can guide the plant manager through the decision-making process while operating the plant. This is achieved imple-menting more efficient generation strategies, optimising energy generation, and obtaining a higher solar thermal fraction.
Figure 1.3: Big Data – From Descriptive to Prescriptive
Forecasting tools are included in a set of appropriated computer tools.
These tools work together with thermal simulation software to estimate the energy generation strategies by determining the energy production mix that minimises the energy cost and optimises the electricity production. This leads to an optimisation of the plant operation and integration of the solar field. Several inputs are required to optimise the plant generation: heating and cooling demand, solar thermal generation, electric energy price, and the behavior of the generation systems as well as the district network and its heat losses under different operating conditions.
This PhD focuses on the operation optimisation of a hybrid power plant which covers the demand of a DHC and the maximisation of solar generation.
To achieve this, this thesis must develop an electricity price for the Spanish market and a thermal demand forecasting tool which works together with an energy simulator. The simulator determines generation strategies by op-timising the production mix that minimises the energy cost and maximises the renewable energy fraction. This leads to an optimization of the power plant’s operation and integration of the solar field. Parc Bit is the power plant under study and is in Palma of Majorca, Spain. The power plant can generate heating, cooling and electricity. Thus, the power plant obtains rev-enue by injecting electricity into the grid and supplying thermal energy to the DHC. To maximise the plant’s revenue, it is necessary to develop
algo-rithms that can provide energy generation strategies to meet generation and demand curves.
An EMS is developed to provide the power plant manager with optimal generation strategies. The tool is developed jointly with POLITO and can optimise a multi-energy node power plant at different time horizons.
This simulator requires information such as thermal and electric demand to fulfil, climatic conditions, power plant configuration, and machine behavior at different generation points. As a result, the tool provides the schedule of the generation machines, primary energy consumption, and total revenue for the time horizon under consideration.
A two-cores forecasting tool was developed based on the auto regressive integrated moving average with explanatory variable (ARIMAX) and arti-ficial neural networks (ANN) models to obtain the future electricity prices of the Spanish wholesale energy market and the DHC’s thermal demand.
Those values are fed to the optimisation tool to determine future generation strategies.
Heat losses in the DHC distribution are considered to be part of the ther-mal load to be fulfilled by the power plant. A calculation tool is developed in order to accurately estimate the thermal losses in a four pipes DHC sys-tem under different working circumstances. In such a syssys-tem, it is crucial to consider the influence between heating and cooling pipes and their working temperatures. The tool optimises the supply cost, taking into consideration the cost of generation for the thermal loss and the electricity cost of the pumps’ circulation of the water in the network. A solar generation fore-caster is developed, using the two forecasting cores (ARIMAX and ANN) to estimate the future solar thermal generation that can be expected at Parc Bit. Using the solar thermal forecaster enables the generation to be fed to the optimiser, included in the future generation schedule. Therefore, the so-lar fraction can be maximised by avoiding overlaps with the CHP’s thermal generation schedule.
To clarify the general idea of the thesis, a graphical representation is presented in Figure 1.4. To summarise the aims, the following tasks must be carried out:
Design of an structured query language (SQL) data base (DB)
Development of an energy price forecaster
Development of a renewable energy system (RES) forecaster
Development of a demand forecaster
Installation of dataloggers to register demand data
Development of an EMS
Design of generation strategies
Scheduling and control of three-way valves for supply temperature
Smart Energy Meters
Decision Maker Core
Demand Information Energy
Figure 1.4: Graphical representation of thesis aims
Different computing tools are used in this thesis. The aim of the thesis is to develop an EMS which is composed of different tools.
This thesis covers the setting, configuration and usage of the forecasting tools. However, it does not cover the development of new forecasting models.
The author configures and tests the best configurations but does not develop new methodology. The forecasting models used are off-the-shelf models in-cluded in Matlab and based on ANN and ARIMAX. In this work, these models are also compared and studied, although they are not developed.
This thesis will develop several parts of the power plant optimiser tool.
Nevertheless, the solver used in the tool is developed by Gurobi, solving constraint integer programs (SCIP), or Matlab; this work only configures and uses the solver.
As part of the collaboration with POLITO, this thesis helps to develop several aspects of the EMS, namely XEMS13. The aspects of the EMS improved are:
Development of the cooling node, specially the performance of cooling generators on partial loads and the impact of cooling output tempera-ture
Development of condensing node, including cooling towers, dry coolers and geothermal systems as heat sinks
Usage of climatic data such as ambient temperature and relative hu-midity affecting condensing power calculations
Inclusion of thermal mass of the DHC as thermal energy storage
Calculation of Heat Loss (HL) in thermal energy distribution
Development of optimal energy supply considering HL, electric pump-ing cost and supply temperature