Nadeem ALKURDI

Currently, electricity shortages is a major global problem due to the increasing demand for electricity, which is why the implementation of renewable energy is an important alternative solution to meet our electricity needs, reducing greenhouse gas (GHG) emissions to combat climate change and mitigate dependence on fossil fuel resources. So planning the transition to renewable energy is an essential ongoing strategy to meet our demand needs, whether the electricity grid is connected to the national grid or off-grid in rural areas. Most countries have already begun to reinforce their energy infrastructures to power them from sustainable renewable energy resources, but limited potential resources could halt this deployment.

Today, the integration of different renewable energy resources into the electricity grid is a major challenge for ensuring grid stability. Secondly, the implementation of high-efficiency renewable energy systems requires significant decision-making support to encourage the various parties to invest more. Renewable energy planning needs to be assessed on the basis of technical, economic and socio-environmental criteria. Energy planning cannot be carried out without reference to energy models. Renewable energy system models are considered as an important solution to solve many complex energy planning problems in the world at different periods where the intermittency of some resources (PV, wind ...) could be replaced by other continuous resources (biomass, Hydro ...) to meet energy demand and ensure grid stability. A wide variety of energy models are used to meet different energy objectives, particularly electricity. In addition, these energy models could follow either the deterministic approach using robust models to handle data uncertainty, or the stochastic approach which uses probabilities under uncertainty. Both approaches have been widely used and recommended for renewable energy planning.

The main objective of this thesis is to develop a multi-criteria decision support model for renewable energy transition planning combining different renewable energy resources ensuring maximum production at minimum costs. This thesis will highlight the main concept of the 100 % renewable energy transition by the end of 2030 in French Guiana as it is an overseas territory where there is a challenge in developing the energy scenario by 2030 and the current power generation facilities cannot feed the increasing electricity demand with limited resources but also, other renewable energy facilities are needed to replace the fossil fuel power plants by the end of 2030. In summary, this thesis will answer the following research question: What are the energy matrix models (solar PV and biomass in our case) that could be defined as an initial baseline plan for the energy sectors in any region or in any other concept, how to optimize different energy production scenarios combining different renewable energy resources for maximum production at minimum costs to satisfy energy needs taking into account the spatio-temporal dimensions of the problem and data?

In the first part of this thesis, we developed an integrated framework (GIS-Robust Optimization) as an excellent decision support tool for determining the best optimal sites for solar photovoltaic power plants and their optimized use of surfaces at the utility scale in French Guiana, mixed with heterogeneous data of geography (spatial constraints), associated costs and temporal constraints. This framework is an efficient IT approach for managing large-scale spatio-temporal data. In addition, an updated version of the first approach (park model) called ring model approach is developed to provide better optimal solutions of a PV ring composed of several solar PV plants with maximum production and minimum costs as well as less risky for the power grid in case of urgent disconnection or blocking of large parks due to incidents that do not affect the whole electricity production compared to the park approach.

In the second part, this thesis will focus on the application of our developed framework (GIS-RO) for biomass, which is a continuous renewable energy resource, complementing our previous studies on the intermittent solar resource. This framework will also select optimal potential biomass sites that ensure maximum production and minimum costs under different constraints. In addition, further future work and improvements to our model developed for each renewable energy planning resource are also discussed and addressed in this section.

Finally, our developed methodology is an excellent decision support tool for selecting the best potential locations for renewable energy plants to be implemented. The results were confirmed for intermittent (solar) and non-intermittent (biomass) renewable energy resources integrated into the electricity grid.

Key words:

Renewable energy planning, spatio-temporal scale, GIS, robust optimisation, site selection, French Guiana

Currently, lack of electricity is a major global issue around the world due to the increase in power demand, that's why implementation of renewable energy is an important alternative solution to feed our electricity needs, reducing Green House Gases (GHG) emissions to fight climate change and to mitigate the dependency on fossil fuels resources. So, renewable energy transition planning is an essential ongoing strategy to feed our demand needs whether the network is grid-connected or off-grid in rural areas. Most countries have already begun to reinforce its energy infrastructure to be fed from sustainable renewable energy resources but the limited potential resources could halt such deployment.

Today, integration of different renewable energy resources to the power network is a major challenge to secure the stability of the grid. Then, implementation of high efficient renewable energy systems requires strong decision making support to encourage different parties for further investment. Indeed, renewable energy planning should be evaluated from the techno-economic-socio-environmental criteria. Thus, energy planning cannot be done without depending on energy models. So, renewable energy system models are considered an important solution to solve many complex energy planning problems around the world at different periods of time where the intermittence of some resources(PV, Wind...) could be substituted by other continuous resources(Biomass, Hydro...) to meet the power demand and ensure the grid's stability. Large variety of energy models are used to satisfy different energy targets especially electricity. Moreover, these energy models could follow either the deterministic approach using robust models to handle the uncertainty in data or stochastic approach that use probabilities within uncertainty. Both approaches have been widely used and recommended for renewable energy planning.

The main objective of this thesis is to develop a multi-criteria decision support model for renewable energy transition planning combining different renewable energy resources ensuring maximum production at minimal costs. This thesis highlights the main concept of 100 % renewable energy transition by the end of 2030 in French Guiana as it is an overseas French territory where there is a challenge in the development of the energetic scenario by 2030 and the current energy production facilities cannot feed the increase in power demand within limited resources but also, other energy facilities from renewable energies resources are required to replace the fossil fuels plants by the end of 2030. As a summary, this thesis will answer the following research question: What or which are the energy matrix models (Solar PV and Biomass in our case) that could be set as an initial basic plan for the energy sectors in any region or in other concept, how to optimize different energy production scenarios combining different renewable energy resources of maximum production at minimal costs in order to satisfy the energy needs taking into account the spatio-temporal dimensions of the problem and data?

In the first part of this thesis, we've developed an integrated (GIS-Robust Optimization) framework as an excellent decision support tool to determine the best optimal sites of PV solar plants and their optimized land use at utility scale in French Guiana mixing the following heterogeneous data of geographic(spatial constraints), related costs and temporal constraints. This framework is an efficient computational approach handling large scale of spatio-temporal data. Moreover, an updated version of the first approach (park model) called ring approach model is developed to provide better optimal solutions of a PV ring composing of multiple solar PV plants of maximum production and minimal costs as well as less risky to the power network in case of urgent disconnection or blocking of large parks due to incidents which does not impact the whole power production as compared to park approach. In the second part, this thesis is focused on the application of our developed framework (GIS-RO) for biomass which is dispatchable resource of renewable energy as a complement to our previous studies regarding intermittent solar resource. This framework will also select the optimal biomass potential sites that ensures maximum production and minimum costs which are subjected to different constraints. Also, further future works and improvements of our developed framework for each renewable energy planning resources are also discussed and handled in this part.

Finally, our developed methodology is an excellent decision support tool to select the best potential locations of renewable energy plants in order to be implemented. Results have been confirmed for both intermittent (Solar) and non-intermittent (Biomass) renewable energy resources integrated to the power network.

Keywords: Renewable energy planning, spatio-temporal scale, GIS, robust optimization, site selection, French Guiana