Bogotá - Ingeniería - Doctorado en Ingeniería - Ingeniería Eléctrica · 2022
Operational planning of smart microgrids considering intraday markets
Environmental concerns and sustainable development promote the adoption of smart microgrids (SMGs). However, economic interests promote an increase in income, which can result in non-optimal situations, such as non-supply of demand, the formation of monopolies and the formation of essential agents to supply demand at peak times. In this context, this research analyses a SMG that negotiates energy commitments with intraday markets and binding dispatch. In the same way, this model quantifies penalties for uncertainty of renewables with intraday markets. Besides, the profits are estimated associated with managing the distributed generation, charging and discharging of energy storage systems, a battery swapping station and residential electric vehicles. This model introduces uncertainty in the operational planning problem of a SMG related to (1) renewable generation, (2) demand forecasting, (3) market price variations, (4) planning of electric vehicle trips and (5) battery demand forecast in an electric vehicle station. The literature shows that, due to the complexity of the problem, computational intelligence provides sub-optimal solutions efficiently, resulting in the development of the advanced metaheuristics called VNS-DEEPSO, which is a combination of the Variable Neighbourhood Search (VNS) and Differential Evolutionary Particle Swarm Optimization (DEEPSO) algorithms. The results show demand management strategies, such as reduction of maximum loads, demand supply restrictions satisfactorily met, market power indicators that prevent the emergence of monopolies and pivoting agents, and a greater number of intraday markets with equally time intervals spaced that show a reduction in costs due to the uncertainty of renewables. Finally, the results of this research will constitute a tool to make decisions in smart microgrids and will help to evaluate the implementation of intraday markets in future research.
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Contenido
- Introductionp. 21
- Contextualization of smart microgrid modelsp. 25
- Backgroundp. 25
- Literature reviewp. 27
- 1.2.2 Heuristic and deterministic approachesp. 31
- 1.2.3 Planning considering marketsp. 31
- 1.2.4 Proposed modelp. 32
- 1.2.5 Problem statementp. 33
- Objectivesp. 36
- 1.3.1 General objectivep. 36
- 1.3.2 Specific objectivesp. 37
- Scopep. 37
- Overview of contributionsp. 39
- Thesis outlinep. 24
- 1.6.1 Chapter 2p. 41
- 1.6.2 Chapter 3p. 42
- 1.6.3 Chapter 4p. 42
- 1.6.4 Chapter 5p. 42
- 1.6.5 Chapter 6p. 42
- 1.6.6 Conclusions, recommendations and future worksp. 42
- Heuristic optimization of smart microgridp. 44
- Overview of heuristic optimization algorithmsp. 46
- 2.2.1 Chaotic Evolutionary Particle Swarm Optimization CEPSOp. 47
- 2.2.2 Differential Evolutionary Particle Swarm Optimization DEEPSOp. 47
- 2.2.3 Differential Evolution with Stochastic Selection DESSp. 47
- EVDEPSOp. 19
- 2.2.5 Firefly Algorithm FAp. 49
- DEEPSOp. 19
- 2.2.7 Improved Differential Evolution IDEp. 50
- 2.2.8 Particle Swarm Optimization with Global Best Perturbation PSO-GBPp. 50
- 2.2.9 Unified Particle Swarm Optimization UPSOp. 50
- 2.2.10 Variable Neighbourhood Search VNSp. 51
- DEEPSOp. 19
- Implementation of VNS-DEEPSO algorithmp. 52
- 2.3.1 Implementation of VNS algorithmp. 52
- 2.3.2 Implementation of DEEPSO algorithmp. 57
- Comparative analysis among optimization algorithmsp. 59
- Statistical analysis among optimization strategiesp. 66
- Model of uncertainty costsp. 71
- Uncertainty costs of solar energyp. 71
- 3.1.1 Mathematical formulation of uncertainty costsp. 71
- 3.1.2 Probability functionsp. 73
- 3.1.3 Conditions of photovoltaic generationp. 74
- 3.1.4 Probability functionsp. 73
- Intraday markets considerationp. 79
- Market structurep. 79
- Modelling of probabilistic scenariosp. 80
- Scenario reductionp. 81
- Outlook for intraday marketsp. 82
- Smart microgrid modelp. 83
- Representation of smart microgrid modelp. 83
- Formulation of smart microgrid modelp. 85
- 5.2.1 Fitness functionp. 17
- Results and contributionsp. 101
- Smart microgrid model and intraday marketsp. 101
- penalty of uncertainty costs by PV generationp. 102
- Implementation of intraday marketsp. 105
- Comparative analysis between this research and previous worksp. 110
- Achievement of objectivesp. 112
- Conclusions, recommendations and future worksp. 42
- Conclusionsp. 114
- Recommendationsp. 115
- Future worksp. 115
- A. Annex: Smart microgrid case studyp. 115
- A.1 Modelling of objective functionp. 118
- A.2 Smart microgrid constraintsp. 119
- A.3 Uncertainty formulation in smart microgridp. 121
- A.4 Encoding of the case studyp. 121
- B. Annex: Formulation of penalty costs by uncertaintyp. 125
- B.1 Penalty costs by underestimation in PV generationp. 125
- B.2 Penalty costs by overestimation in PV generationp. 130
- C. Annex: Validation of the uncertainty costs for residential PV generatorsp. 133
- C.1 Monte Carlo cost in the case of residential PV generatorsp. 136
- C.2 Error in the case of residential PV generatorsp. 136
- D. Annex: Uncertainty costs in external PV generator of smart microgridp. 139
- E. Annex: Implementation of market power indicators in smart microgridp. 142
- E.1 Formulation of market power indicators in smart microgridp. 140
- E.2 Comparison of smart microgrid with and without market powerp. 145
- Annex: Formulation of the load shedding constraintsp. 147
- F.1 Formulation of the 0-1 knapsack problemp. 145
- F.2 Formulation of load shedding constraintp. 148
- F.3 Outcomes comparisonp. 149
- Appendixp. 155
- Contribution of the proposed workp. 33
- General objective summaryp. 37
- Methodological scopep. 38
- Summary of the overall contributions [87]–[96]p. 39
- Pseudocode of VNSp. 53
- Pseudocode of CCM [78]p. 54
- Pseudocode of FLSM based on [78]p. 56
- Representation of particle velocity based on [77]p. 58
- Pseudocode of DEEPSO based on [77]p. 59
- Objective function tested of all algorithms in each runp. 63
- RI tested of all algorithms in each runp. 65
- Comparison with average ranking indexp. 66
- Multiple comparison of statistical RIp. 68
- Outline of the case studyp. 79
- Markets schedulingp. 80
- Prices frequency: binding dispatch (a) and intraday market 1 (b)p. 82
- Prices frequency: intraday market 2 (a) and intraday market 3 (b)p. 82
- Representation of energy transactions in SMGp. 84
- Structure of the problem to solvep. 85
- Clustering of scenarios for EV marketp. 96
- Clustering of scenarios for the driven pattern of EVp. 97
- Overview of the two stages of optimization for the SMG case studyp. 102
- Scheduling of the generation and EVs during 24 hoursp. 103
- Scheduling of the ESSs and loads with DR during 24 hoursp. 103
- Indicators of market power during 24 hoursp. 104
- Penalties by uncertainty costs in the SMGp. 105
- Mean costs of RI without UCs and UCs of PV generationp. 106
- Intraday markets participation and binding dispatch (part a)p. 108
- Intraday markets participation and binding dispatch (part b)p. 109
- Participation of 6 Intraday markets and a binding dispatch (part c)p. 109
- Achievement of objectivesp. 112
- SMG optimization problem with an aggregator based on [17]p. 117
- Residential 25-bus SMG in a 400V system based on [175]p. 120
- Residential PV generators in 24 hp. 133
- overestimated and underestimated costsp. 137
- Simulation of Monte Carlo for PV generation casep. 135
- Herfindahl-Hirschman index for SMG modelp. 142
- Load profile with DRp. 143
- Ranking index in 20 runsp. 144
- Load forecast for 24 hp. 146
- Maximum demand in the SMGp. 147
- Discretization in 68 loadsp. 147
- Discretization in 17 loadsp. 148
- Exponential response curve for first order system [184]p. 153
- Residential load profile with DRp. 150
- Load shedding for all customersp. 150
- Ranking index in 20 runsp. 144
- Reduction percentage of load shedding with DRp. 152
- Execution time for 20 runsp. 157