A Two-Step Energy Management Method Guided by Day-Ahead Quantile Solar Forecasts: Cross-Impacts on Four Services for Smart-Buildings
Abstract
:1. Introduction
1.1. Energy Management of Microgrids
1.2. Resources Scheduling and Forecasts
1.3. Services in Smart Buildings
- Proposing a service (i.e., grid-commitment), intended to decrease (and serve as a measure of) the degree of uncertainty posed by a microgrid using IRES to the utility grid, regarding its daily power requirements.
- Acknowledging that some services (e.g., grid-commitment) are more favored by eccentric (i.e., pessimistic) forecasts rather than unbiased forecasts.
- The use of quantiles as eccentric (deterministic) forecasts, obtained from an analogs ensembles method, with low computational burdens/time and good performance with respect to benchmark probabilistic forecasting methods.
- Proposing an energy management framework for microgrids, which allow the maximization of the grid-commitment service by means of an underlying rule-based layer, preceded by a day-ahead scheduling optimization-based layer, where another service (i.e., energy cost, carbon footprint, or grid peak power) can be also favored. In this way, the interests of both, microgrid users and distribution system operator, are taken into account during the energy management process.
- Acknowledging that also some external (seasonal) conditions change the forecasting requirements when optimizing for a given service, which again highlights the usefulness of quantile forecasts for customizing optimization/energy management strategies.
2. Materials and Methods
2.1. The Analogs Ensembles Method
2.2. Benchmark Forecasting Methods
2.3. Services and Performance Indicators
2.3.1. Service 1: Reduction in Energy Costs ()
2.3.2. Service 2: Reduction in Electricity Carbon-Footprint (CO)
2.3.3. Service 3: Day-Ahead Power-Commitment with the Utility Grid (GC)
2.3.4. Service 4: Reduction of Grid Contracted-Power ()
2.4. Two-Step Proposed EMS
2.4.1. EMS Scheduling Module
2.4.2. EMS Balancing Module
2.4.3. Reference Strategies
3. Results
3.1. Performance of the Proposed Scheduling Strategies
3.2. Optimistic and Pessimistic Forecasts: The Versatility of Quantile Forecasting
3.3. Optimizing the Services: Finding the Best-Suited Quantile Forecasts
3.4. Impact in Performance of Targeting One Service over the Non-Target Services
3.5. Seasonal Performance Optimization and Analyses
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A. Nominal and Adjusted Values for Battery and PV Energy
Appendix A.1. Energy Cost Calculations
Appendix A.2. Energy CO2 Content Calculations
Appendix B. Optimization Algorithms
Penalization Weights | |
---|---|
K | 1 × |
L | 5 |
M | 1 × |
Hyper-Parameters | |
# of Iterations | 300 |
Population size | 1000 |
Mutation probability | 100% |
# of mutating chromosomes | 1 |
Mating pool size (# of parents) | 100 |
Appendix B.1. Energy Cost Minimization
Appendix B.2. CO2 Content Minimization
Appendix B.3. Grid Peak-Power Minimization
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EMS Strategy | Type | Algorithm | Target Objective | Objective Function/Rules | Possible Forecasts | |
---|---|---|---|---|---|---|
Scheduling (SCH) | Optimization based | Genetic | Minimize Energy Cost | See Equation (A11) | PF (Perfect Forecast) PE (Persistence) NWP (Numerical Weather Prediction) | |
Optimization based | Genetic | Minimize CO2 content | See Equation (A12) | AnEn=0.1−AnEn=0.9 | ||
Optimization based | Quadratic Programming | Minimize Grid Peak Power | See Equation (A13) | (Analogs-Ensembles quantile forecasts) | ||
Balancing (BAL) | Rule based | Rules | Maximize Grid commitment | See Figure 5 | No forecasts used |
Proposed Strategy | Reference Strategy | Performance | ||
---|---|---|---|---|
NO MG | – | Indicator | ||
-AnEn | −14.5% | −10.3% | −8.8% (AnEn) | EC (€/kWh) |
-AnEn | +3.3% | −6.0% | −1.6% (AnEn) | (gCO2/kWh) |
-AnEn | −36.5% | −9.0% | −36.5% (AnEn) | GPP (€) |
Scheduling Strategy | Performance Indicator | AnEn=0.1 (Pesimistic Forecast) | AnEn=0.9 (Optimistic Forecast) | PE (Unbiased Forecast 1) | NWP (Reference Forecast) | PF (Most Accurate Forecast) |
---|---|---|---|---|---|---|
EC (€/kWh) GC (%) | 0.297 99.9 | 0.217 92.1 | 0.176 96.2 | 0.169 99.1 | 0.154 100 | |
CO (gCO2/kWh) GC (%) | 73 99.9 | 89 88.1 | 65 94.3 | 64 98.5 | 63 100 | |
GPP (kW) GC (%) | 15 99.7 | 18 90.8 | 15 95.3 | 18 99.1 | 15 100 |
EMS Intended to: | ECmin-AnEn=0.5 Minimize EC favor GC | CO2min-AnEn=0.3 Minimize CO2 favor GC | GPPmin-AnEn=0.4 Minimize GPP favor GC | ECmin-AnEn=0.1 Minimize EC favor GC | ||||
---|---|---|---|---|---|---|---|---|
Performance indicator | % respect | % respect | % respect | % respect | ||||
to the best: | to the best: | to the best: | to the best: | |||||
EC (€/kWh) | 0.165 | 0.0 | 0.173 | +4.8 | 0.177 | +7.3 | 0.297 | +80.0 |
CO2(gCO2/kWh) | 65 | +3.3 | 63 | 0.0 | 64 | +1.9 | 105 | +66.5 |
GPP (kW) | 30 | +57.4 | 30 | +57.4 | 15 | 0.0 | 30 | +57.4 |
GC(%) | 98.7 | −1.2 | 99.4 | −0.5 | 99.3 | −0.6 | 99.9 | 0.0 |
Performance Indicator | Best Winter EMS | Best Spring EMS | Best Summer EMS | Best Autumn EMS |
---|---|---|---|---|
EC (€/kWh) | ECmin-AnEn=0.6 | ECmin-AnEn=0.5 | ECmin-AnEn=40:50 | ECmin-AnEn=50:60 |
CO (gCO2/kWh) | COmin-AnEn=0.3 | COmin-AnEn=0.4 | COmin-AnEn=0.4 | COmin-AnEn=0.1 |
GPP (kW) | GPPmin-AnEn=0.2 | GPPmin-AnEn=0.2:0.3 | GPPmin-AnEn=0.1:0.8 | GPPmin-AnEn=0.1:0.9 |
GC (%) | ECmin-AnEn=0.2 | ECmin-AnEn=0.1:0.2 | ECmin-AnEn=0.1 | ECmin-AnEn=0.1 |
Performance Indicator | Winter | Spring | Summer | Autumn |
---|---|---|---|---|
EC (€/kWh) | 0.236 (−1.7%) | 0.114 (−2.6%) | 0.091 (−5.2%) | 0.173 (−2.2%) |
CO (gCO2/kWh) | 58 (−2.1%) | 53 (−0.6%) | 56 (+1.1%) | 89 (−24.1%) |
GPP (kW) | 15 (−16.7%) | 12 (0%) | 9 (0%) | 15 (0%) |
GC (%) | 100 (+0.4%) | 100 (+1.8%) | 100 (+1.6%) | 100 (+0.1%) |
Performance Indicator | EMS Strategy | |
---|---|---|
Seasonal | Annual | |
EC (€/kWh) | 0.150 (−9.1%) | 0.165 |
CO (gCO2/kWh) | 62.8 (−0.5%) | 63.1 |
GPP (kW) | 15 (0%) | 15 |
GC (%) | 100 (+0.1%) | 99.9 |
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Calderon-Obaldia, F.; Badosa, J.; Migan-Dubois, A.; Bourdin, V. A Two-Step Energy Management Method Guided by Day-Ahead Quantile Solar Forecasts: Cross-Impacts on Four Services for Smart-Buildings. Energies 2020, 13, 5882. https://doi.org/10.3390/en13225882
Calderon-Obaldia F, Badosa J, Migan-Dubois A, Bourdin V. A Two-Step Energy Management Method Guided by Day-Ahead Quantile Solar Forecasts: Cross-Impacts on Four Services for Smart-Buildings. Energies. 2020; 13(22):5882. https://doi.org/10.3390/en13225882
Chicago/Turabian StyleCalderon-Obaldia, Fausto, Jordi Badosa, Anne Migan-Dubois, and Vincent Bourdin. 2020. "A Two-Step Energy Management Method Guided by Day-Ahead Quantile Solar Forecasts: Cross-Impacts on Four Services for Smart-Buildings" Energies 13, no. 22: 5882. https://doi.org/10.3390/en13225882