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Abstract
Governments of developing countries are struggling to foster universal electricity access in their territory. Past policies have prioritized the peri-urban areas where the high-density population justifies the costs of extending the national grid, but rarely the same approach is affordable for rural areas, where people live in poor conditions with low demand related to basic needs, like lighting and mobile-phone charging. Within this context, isolated power grids, so called mini-grids, based on renewable sources and limited amount of fuel have demonstrated to provide good quality electricity at a reasonable price, thereby enabling the socio-economic development of the local community. However, the high business risks and payback uncertainties still curb many private and public initiatives. This research aims at de-risking mini-grid projects and reducing the financial burden through the development of advanced planning, sizing and operating methods for rural mini-grids that minimize operating and investment costs, while addressing the stochasticity of load, renewable sources and fuel procurement through Monte Carlo simulations. A novel stochastic operating approach based on Monte Carlo scenarios was proposed, as well as a new classification of stochastic approaches reviewed in literature. Moreover, the effect of using predictive strategies in the design phase has been studied and the results suggested the possible use of simplified strategies to optimally design mini-grids. Finally, a multi-year stochastic dynamic technique is proposed to plan the design of a mini-grid under load growth, which is very steep and unpredictable in newly electrified communities. The methodology enabled deferring the installation of components as the demand grows, thus achieving important savings in term of net present costs over traditional techniques, even more than 35%.
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Summary
Abstract ... 1
Summary... 2
1. Introduction ... 7
2. Developing a mini-grid project ... 13
2.1. Site contextualization ... 13 2.1.1. Regulatory framework ... 13 2.1.2. Financing resources ... 14 2.1.3. Site characterization ... 14 2.2. Business model ... 15 2.3. Tariff system ... 16 2.4. Demand assessment ... 17 2.5. Mini-grid design ... 18 3. Operating strategy ... 21 3.1. Introduction ... 21 3.2. Priority-list approaches ... 26 3.2.1. Load-following strategy (LFS) ... 26 3.2.2. Cycle-Charging strategy (CCS) ... 27
3.3. Predictive rolling-horizon approach ... 30
3.3.1. Description ... 30
3.3.2. Rolling-horizon dispatching (RHS) ... 32
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3.4. Standard stochastic optimization (SO) ... 37
3.4.1. Description ... 37
3.4.2. Mathematical formulation ... 37
3.5. Proposed Aggregating-Rule-based Stochastic Optimization (PARSO) ... 41 3.5.1. Description ... 41 3.5.2. Initialization ... 42 3.5.3. First stage ... 42 3.5.4. Second stage ... 44 3.5.5. Aggregator ... 45 3.6. Case study... 46 3.6.1. Proposed comparison ... 46 3.6.2. Location ... 46 3.6.3. Reference mini-grid ... 46 3.6.4. Load ... 47
3.6.5. Available renewable energy production ... 48
3.6.6. Rolling-horizon approach parameters... 49
3.6.7. Other parameters ... 49
3.6.8. Comparing procedure ... 50
3.7. Results and discussion – deterministic strategies ... 54
3.8. Results and discussion – stochastic strategies ... 57
3.8.1. Cost and computational time analyses ... 57
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4. Optimal sizing techniques ... 65
4.1. State-of-art ... 65
4.1.1. Uncertainties in sizing methodology ... 65
4.1.2. Fuel tank sizing ... 67
4.2. Model of the mini-grid ... 68
4.3. Fuel procurement strategy ... 69
4.3.1. Fuel logistics ... 69
4.3.2. Fuel procurement ... 70
4.3.3. Model of the fuel delivery ... 71
4.4. Stochastic sizing methodology ... 72
4.4.1. Description ... 72
4.4.2. Objective function ... 74
4.4.3. Outer loop: optimization ... 75
4.4.4. Inner loop: stochastic simulation ... 75
4.4.5. Post-processing of the simulation points ... 76
4.5. Case study... 77
4.5.1. Location ... 77
4.5.2. Parameters ... 77
4.5.3. Load profile ... 77
4.5.4. Available photovoltaic production ... 78
4.5.5. Fuel cost and procurement ... 78
4.5.6. Investment costs and efficiencies ... 79
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4.6.1. Effect of the operating strategy ... 85
4.6.2. Effect of the fuel procurement ... 87
4.6.3. Post-processing of results ... 88
5. Multi-year planning of rural mini-grids ... 93
5.1. Introduction ... 93
5.2. Multi-year load assessment ... 97
5.3. General stochastic multi-year design ... 99
5.4. Stochastic tree-based multi-year design ... 101
5.4.1. Tree model of the multi-year demand and sources ... 101
5.4.2. Upgrading the mini-grid ... 102
5.4.3. Degradation model ... 104
5.4.4. Replacements ... 104
5.4.5. Operating strategy ... 105
5.4.6. Recursive objective function ... 106
5.4.7. Constraints ... 108
5.5. Case studies ... 119
5.5.1. Description and location ... 119
5.5.2. Load and load growth ... 119
5.5.3. Renewable energy production ... 120
5.5.4. Degradation of components and residual ... 120
5.5.5. Other parameters ... 121
5.5.6. Case A: Full tree multi-year model ... 121
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5.5.8. Case C: Simplified multi-year model ... 123
5.5.9. Case DX: 1-year equivalent model ... 124
5.6. Results and discussion ... 127
6. Conclusions and future developments ... 137
Acknowledgments ... 141
