Forecasts and energy management in a microgrid: Impact on services provided by a smart building 

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Nanogrid data output

The nanogrid web interface is able to produce real-time plots of the voltage, current and power of the different resources. Figure 2.6 highlights some of the basic aspects of power systems and the interactions between the different elements in a microgrid. The plot has been generated for a particular day (2019-10-13), where the system was running without any human intervention. Aspects such as the self-balancing ability of the system to match production and consumption, the distribution of power-delivery between the power source and the battery according to the state of charge of the latter, the curtailment performed by the MPPT controller when the PV potential is greater than the demand, the priority given to the load in the use of the available PV energy, among other aspects; can be observed in this figure. All these points help to have a better comprehension of the underlying laws that rule and regulate the functioning of a power system like a microgrid.

Pedagogical outcomes of the nanogrid

With the capabilities of the third -and current- version of the NRLAB NG, a practical experience has been develop with the aim to help students discover and understand the basics about electric power systems, grid interactions as well as microgrids management. The experience assumes no prior knowledge about the subject from the participants, hence it was conceived as a very basic/conceptual practice. However, this experience is enough to show the most important features and possibilities that the NG offer as a didactic tool. The practical experience is presented in the following sections, which brings out the added value of the NG as a pedagogic tool.
The text of the full student guide developed for this practical experience can be consulted in Appendix D.

Experimental procurement of Joule losses and the equivalent resistance of the circuit

At the beginning of the practice, an introduction is made where the main learning objectives of the practice are briefly explained and students are guided to make the proper electrical connections of the elements required for the practical experience (i.e. PV panel, battery, load and power source). Then, in the first part of the experience, students are familiarized with the concepts of Joule losses in an electrical system. For this purpose, they are asked to connect only the power source and the load to the common bus of the NG and to increase the load from 10W to 90W, taking note of the currents, voltages and powers. With this data, they perform a plot where, by fitting the experimental curve obtained, they can compute the approximate equivalent resistance of the circuit Req, that includes all the ohmic losses due to wiring and the cross-section of the cables. The equation of Joule losses used to compute the equivalent resistance of the circuit is presented in equation 2.2. Pjoule = Req ·I2 (2.2).
Then, students are asked to confirm the value obtained for the equivalent resistance, by measuring it directly with a multimeter. Usually they obtained results with a +/-10% of error margin, which is reasonable taking into account the accuracy of the measurement instrument used. An example of the fitting obtained by a group of students is shown in figure 2.11, where the blue curve represent the polynomial fit of the measured power-loss values (difference between the power delivered by the source and the power consumed by the load), presumably due to Joule losses. They are asked to confirm this fact by doing the experimental fitting, and finding the corresponding value for Req, which they further validate by a direct measurement. The detailed procedure and tables to be filled out for this part of the experience can be found in the Joule Losses section of the student guide presented in Appendix D.

Understanding the basics of power flows and its manipulation

The core of the practical experience is devoted to understand some basic principles regarding the electrical interactions between the elements (also called distributed energy resources or DERs) of an electrical power system, which are exemplified with the nanogrid. More specifically, the learning objectives addressed in this part are:
• Voltage as a tool to manipulate power flows.
• Current limiting as a tool to manipulate power flows.
• Impact of load variations in the power flows.
• Impact of PV production variations in the power flows.
• Natural self-balancing response of an electric power system to variations in production and consumption.
There are some types of electrical loads whose power consumption is voltage-dependent. In general, any device with a fixed internal resistance that uses the joule effect to produce heat or light, will vary its consumption if the feeder voltage levels change. Within this category we can find drying, cooking, electric water-heating and lighting appliances, that altogether can represent an important percentage of the total consumption in an electric power system. Therefore, keeping the levels of voltage as constant as possible is one of the variables to keep an eye on in an electrical system like a MG.
To exemplify this fact, students are asked to branch only the power source and the programmable electronic load to the NRLAB-NG. Using the different load modes of this device (e.g. fixed resistance, fixed power) they are able to emulate the voltage dependent and non-dependent types of loads. Under these scenarios, students are asked to vary either the voltage of the power source (keeping a constant load power), or the load power (keeping the output voltage of the power source constant). The response of the system in each case is noted and its real-life applicability and implications are discussed. For instance, it is highlighted the fact that voltage variations in a MG can affect consumption (or vice-versa), hence, the importance of having a voltage control system in any electrical power grid.
This short demonstration serves to introduce the first main learning outcome, which is the use of voltage variations in order to manipulate power flows. To exemplify this topic, students are asked to connect the battery, power source and load to the main bus. The battery is a non-linear element whose behaviour is not always well understood. By varying the output voltage of the power source, the battery is taken from a charging to a discharging regime, to show students how the power exchanged by the battery can be -indirectly- manipulated by means of voltage variations. The concepts of open circuit voltage (Voc), state-of-charge (SoC), and the correlation between SoC and Voc are also tackled. The importance of the battery as an element that can react very quickly to compensate sudden power imbalances in the grid is also highlighted with this exercise.
An example of this exercise is shown in figure 2.12, where the effects of variations in the voltage of the power source (yellow curve) over the charging state of the battery are demonstrated. Results show how the battery can be taken from a discharging to a charging state by means of setting the output voltage of the power source above or below the Voc of the battery, which has been previously measured at the beginning of the exercise. In this figure, the effect of Joule losses can also be observed as the difference in the power values between the power source and the load (that should be the same when the battery power is zero, if no Joule losses were present).

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The scheduling interactive table

In the third part of the dashboard, the user makes its consumption scheduling making use of the scheduling interactive table, as depicted in figure 2.18. For every 30-minutes timestep, the user can decide how to split the required daily consumption of the household appliances proposed. Each appliance must be used a certain amount of hours during the day, assuming an hypothetical-average profile of use. This is indicated in the last-right column of the table. This interactive table allows the user to schedule consumption following certain restrictions, at the same time that permits to check the compliance with the time of use of each appliance, maximum feeder power ratings and SoC of the battery. The maximum and minimum SoC levels of the battery can also be set by the user. The user will be penalized if those limits are surpassed. The platform will not allow the user to run the simulation until all the requirements are fulfilled.
There is a based-load that corresponds to the non-dispatchable consumption (e.g. the refrigerator or the electronic devices that are always plugged), which cannot be modified. The minimum consumption period that can be selected for each home appliance is 30 minutes, as mentioned before. Most of the appliances are considered to have a fixed power rate, except for the water heater that is assumed to have a flexible power setting between zero and its nominal power. This makes this element very versatile for energy management purposes as it can be used as storage of thermal energy in the system. It is considered to have negligible thermal losses, then the total daily-energy required to heat the water can be supplied at any moment of the day. The only constraint in this case (and for all the other appliances too), is that the maximum power drawn from the main feeder cannot surpass 5kW at any given moment.
The daily habits and routine of a person plays an important role in this exercise, which is also remarked during the explanation of the experience. Students are asked to use the common sense to schedule the different devices according to a realistic daily routine of a person that is working from home on that specific day. However, there are as many possibilities as students in the room, so what for one student might be a normal time to prepare the meal or take a shower, for other student might be rare or unrealistic. This analysis is part of the debrief that is performed at the end of the exercise when the results, obtained by the different students, are compared.

Performance indicators calculation as the game scores

There are several performance indicators that are computed using equations 2.3 to 2.7. Each one of them is related to a service that the MG provides to the users and could be in itself, an optimization objective for the game. There is also an overall performance score that is an average of those five individual scores, as the example given in figure 2.19 shows.
SELF-CONSUMPTION = EPVused ∗100 (2.3).
PV SELF-SUFFICIENCY = EPVused ∗100 (2.4).
Eload BATTERY BALANCE = 100 − SoC f inal −SoCinitial (2.5).
E+ BATTERY USE = 100 − battery ∗100 (2.6).
Eload EC@lowest price GRID COST = grid ∗100 (2.7).
where EX stands for the energy exchanged by the resource X, Ebattery+ stands for the energy delivered by the battery (i.e. only on discharge regime), ECgrid is the total cost of the energy bought to the grid while ECgrid@lowest price represents the total cost of the energy bought to the grid if bought at the lowest price possible.
The self-consumption score evaluates how much of the potential PV production available is actually used. Even when an MPPT controller is assumed to be used, when there is not enough consumption or storage capacity, any PV power available has to be curtailed. This leads to a self-consumption rate below 100%. Ideally, all the solar potential should be used at all times in order to maximize the self-consumption. This in turn, will decrease the need of buying electricity from the grid which has also an impact on the grid cost indicator.
The self-sufficiency score evaluates the amount of the total energy consumption of the day that was supplied by the PV panels. The closer to 100% the more energy-autonomous the system is, meaning that less energy has to be bought from the grid.
The SoC of the battery at the end of the day, is a criteria that can be chosen arbitrary depending on the energy management strategy. It is intended to assure the sustainability and availability of battery capacity throughout the week. In this case, the SoC at the end of the day is asked to be left at the same level as it was at the beginning of the day (SoC f inal = SoCinitial , 60% default value). In this way, every other day will have the same battery capacity available to perform the daily scheduling. The further the SoC from its target value (either above or below), the bigger the penalization.

Table of contents :

List of Figures
List of Tables
Nomenclature
1 General introduction 
1.1 A building-size microgrid emulated in a experimental lab
1.2 The uncertainty associated to a deterministic solar forecast
1.3 Energy management of a microgrid under solar production uncertainty .
2 Experimental nanogrid development with pedagogical and demonstrator purposes
2.1 Introduction
2.2 Materials and methods
2.3 Nanogrid data output
2.4 Pedagogical outcomes of the nanogrid
2.4.1 Experimental procurement of Joule losses and the equivalent resistance of the circuit
2.4.2 Understanding the basics of power flows and its manipulation .
2.4.3 Recapitulation of learning outcomes
2.5 The load scheduling on-line game
2.5.1 The game Dashboard
2.5.2 The scheduling interactive table
2.5.3 Performance indicators calculation as the game scores
2.6 Conclusions
2.7 Current and future developments and functionalities
3 Uncertainty estimation for deterministic solar irradiance forecasts based on analogs ensembles 
3.1 Introduction
3.2 From deterministic to probabilistic forecasts
3.3 Evaluation metrics considerations
3.3.1 Required properties for skillful probabilistic forecasts
3.3.2 Proposed evaluation framework
3.4 The Analog Ensemble method retrieval
3.4.1 The Analogs method principle
3.4.2 Considered datasets
3.4.3 Predictors selection
3.4.4 Similarity criteria for analog selection
3.5 Benchmark methods
3.5.1 Climatology and persistence
3.5.2 Monthly Climatology
3.5.3 ECMWF ensembles
3.6 Results
3.6.1 Performance of the AnEn regarding the number of members
3.6.2 Ensembles dispersion analysis and interpretation
3.6.3 Forecasts comparison
3.7 Conclusions
3.8 Future perspectives
4 Forecasts and energy management in a microgrid: Impact on services provided by a smart building 
4.1 Introduction
4.2 Objectives
4.3 Use case description
4.4 Services and performance indicators
4.4.1 Service 1: Reduction in energy costs
4.4.2 Service 2: Reduction in electricity carbon footprint
4.4.3 Service 3: Day-ahead grid power commitment
4.4.4 Service 4: Reduction of grid peak power
4.5 Proposed two-step energy management system
4.5.1 The optimization algorithms
4.5.2 The scheduling module
4.5.3 The balancing module
4.5.4 Reference strategies
4.6 Performance evaluations
4.6.1 Added value of scheduling
4.6.2 Impact of deterministic forecasts uncertainty
4.6.3 Contribution of using quantile forecasts
4.6.4 How optimizing for one service affect the others
4.6.5 Seasonal performance optimization and analyses
4.7 Conclusions
4.8 Perspectives for further research
5 General conclusions and perspectives 
Bibliography

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