Computer Tool for assessing the power generation ...

Computer Tool for assessing the power generation-mix in residential Nearly Zero-Energy Buildings Félix Manuel Lorenzo Bernardo Edwin Xavier Domínguez Gavilanes

Contents 1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.1

Nearly-Zero Energy Buildings

6

1.2

Distributed Generation

7

1.3

Self-Consumption

8

1.4

Power Reliability

9

1.4.1

Functional Zones and Hierarchical Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.4.2

Monte Carlo Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.4.3

Methods in Reliability Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.4.4

Efficiency of Monte Carlo methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.4.5

Convergence characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.4.6

Two-State Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2

Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.1

Goals

3

Power Generation Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.1

Overview

17

3.2

Photovoltaic Model

17

3.2.1

Zenith angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

16

3.2.2

Solar declination angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.2.3

Sunset hour angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.2.4

Weather modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.2.5

PV system modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.3

Wind Turbine Generator Model

3.3.1

Wind speed distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.3.2

Wind turbine model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.4

Conventional Generators Modeling

4

Power Demand Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.1

Overview

30

4.2

Domestic Load Profile

31

4.2.1

Daily energy consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.2.2

Unoccupied period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.2.3

Usage hours for the different loads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.2.4

Consumption profile for the different loads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.2.5

Dwelling and Building Demand profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

5

Energy Storage Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

5.1

Overview

41

5.2

Battery Model

41

5.3

Grid Energy Exchanges and Flowchart

42

6

Microgrid Reliability Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

6.1

Overview

45

6.2

Generating System Adequacy Assessment

45

6.2.1

System State Duration Sampling Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

7

Economic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

7.1

Overview

24

27

51

4 7.2

Traditional Energy Purchase

7.2.1

Access tariffs structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

7.3

Self-Consumption

7.3.1

Installation, maintenance and operation costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

7.3.2

Energy exchanges with the grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

8

Graphical User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

8.1

Developed Computer Tool

9

Study Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

9.1

Case 1

9.1.1

Reliability Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

9.1.2

Economic Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

9.2

Case 2

9.2.1

Reliability Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

9.2.2

Economic Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

9.3

Case 3

9.3.1

Reliability Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

9.3.2

Economic Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

10 Bibliography

52

54

57

60

66

71

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Books

77

Articles

78

Nearly-Zero Energy Buildings Distributed Generation Self-Consumption Power Reliability Functional Zones and Hierarchical Levels Monte Carlo Method Methods in Reliability Evaluation Efficiency of Monte Carlo methods Convergence characteristics Two-State Model

1. Introduction Buildings account for around 40% of total energy consumption and 36% of CO2 emissions in Europe [1]. Therefore, the reduction of energy consumption and the use of energy from renewable sources in the buildings sector constitute important measures which are needed to reduce energy dependency and greenhouse gas emissions. The mitigation potential of emissions from buildings is important and as much as 80% of the operational costs of standard new buildings can be saved through integrated design principles, often at no or little extra cost over the lifetime of the facilities [16]. The European Union Directive 2010/31/EU [42] related to the the energy performance of buildings (EPBD) demands that “Member States shall ensure that by 31 December 2020 all new buildings are nearly zero-energy buildings; and after 31 December 2018, new buildings occupied and owned by public authorities are nearly zero-energy buildings”. In the directive, “nearly zero-energy building" (nZEB) is defined as a building that has a very high energy performance. The nearly zero or very low amount of energy required should be covered to a very significant extent by energy from renewable sources, including energy from renewable sources produced on-site or nearby. These requirements to move towards very low-energy buildings will trigger a deep market transformation not only in this sector but also in others, most notably the power sector, as over half of all electricity consumed today is used in buildings. Electricity savings in buildings will have significant benefits for the power sector, permitting to reduce the investment in generation and distribution assets and thus allowing electrical companies to increase their clients without significant network expansions or high investments in new power plants. As a consequence, it is crucial to develop technical tools that enable the quantification and analysis of the environmental, economic and reliability merits or defects of using renewable-distributed power generation systems in buildings. However, due to the intermittence on the use of renewable energies

6

Chapter 1. Introduction

when trying to reduce or avoid the grid electricity consumption, it is often difficult to determine which is the most appropriate array of technologies to implement. For instance, for a particular scenario, after considering location, environmental and supply constraints and given a certain budget; there are a huge number of combinations of renewable generation systems that could be used alone or simultaneously (wind, solar, fuel cells, biomass, etc.). Nevertheless, to take the best advantage of the investment, it is a key aspect to identify which specific distributed generation configuration provides the best reliability and monetary expectations. Hence, the main purpose of this project is to develop a software tool to successfully assess professionals, promoters or companies in the selection of the best renewable-distributed generation-mix suitable to be installed in residential buildings considering technical, economic and reliability aspects. To achieve this goal, firstly, it is necessary to analyze basic concepts and criteria regarding to nearly-zero energy buildings, distributed generation and power reliability.

1.1

Nearly-Zero Energy Buildings The buildings in which we live need to be safe, functional and comfortable, as well as functionally integrated into our urban areas. At the same time, they need to be increasingly energy efficient and environmentally friendly. Meeting all these needs means coming face to face with the building sector as it unfortunately stands today: highly diverse, critically fragmented and with significant inertia to change. For this reason and with the aim of fulfilling the Europe 2020 targets [43] (have a 20% of final energy consumption from renewables and increase energy efficiency by 20% by 2020), the European legislation has set out a cross-sectional framework of ambitious targets for achieving high energy performances in buildings. Key parts of this European regulatory framework are the Energy Performance of Buildings Directive 2002/91/EC (EPBD) [9] and its recast (Directive 2010/31/EU) [10]. This recast demands that "Member States shall ensure that by 31 December 2020 all new buildings are nearly zero-energy buildings; and after 31 December 2018, new buildings occupied and owned by public authorities are nearly zero-energy buildings". The guidance by the EPBD recast is on a general level. In the directive, "nearly zero-energy building" (nZEB) is defined as a building that has a very high energy performance. In response to this challenge, the building design and research community have started to develop efficient nZEB buildings [70] that, on an annual basis, draw from outside sources an amount of energy that is equal to, or a little higher than, the energy produced on site from renewable energy sources. A nZEB building can be dependent or independent of the electrical grid. As discussed by [68] and

1.2 Distributed Generation

7

[4], with the current technology, a grid disconnected nZEB is difficult to implement, both from an economical and technical viewpoint, due to the seasonal mismatch between energy demand and renewable energy supply and also because the need for large storage capacity. In the off-grid approach, the excess of renewable energy collected in the summer is wasted and cannot be used to balance energy needs during the winter period. On the other hand, on a grid connected nZEB any surplus in electricity production is injected into the grid, conversely, when production is insufficient, the building draws from the grid; making the grid connected configuration the most versatile and reliable. An ideal nZEB should have the following features [22]: • Present low building related energy needs (adequate use of natural light and ventilation, have better performance of the building envelope, present optimal passive heating and cooling). • Have efficient building energy systems (including domestic appliances). • Have adequately sized renewable energy systems that are connected to a flexible energy infrastructure (the electrical grid must be able to exchange energy with the building). Nevertheless, it is important to mention that the most logical path towards the "nearly-zero goal" is firstly to reduce the energy demands by means of energy efficient technologies, and secondly to utilize the renewable energy sources (RES) to supply the remaining energy [37]. However, as indicated by Laustsen [28], a nZEB can also be a traditional building supplied with very large renewable energy systems, and if these systems deliver the same amount of energy over a year as the energy use in the building, the goal of "zero energy" is still met. This may be the initial case of existing buildings that are moving to a greener path for instance. It also should be noticed that the allowed minimum energy demand requirement for any nZEB depends very much on its local context and building type [3]. Regarding the renewable sources, they can either be available on the site, e.g., sun or wind, or need to be transported to the site as biomass or hydrogen to be later used by micro-gas turbines and fuel cells respectively. Note that for a particular nZEB and given a fixed budget, there is a large amount of combinations for possible renewable generation systems that could be implemented. Therefore, the main purpose of this project is to provide nZEB designers and promoters with a computer tool that assesses the selection of the best renewable-distributed generation-mix considering technical, economic and reliability concerns.

1.2

Distributed Generation There are several different definitions regarding Distributed Generation which are used as well in literature and in practice. This can yield to confusion as these definitions are often subjected to the

Chapter 1. Introduction

8 Size

Power Rating

Micro

Distributed Generation

1 W to 5 kW

Small

Distributed Generation

5 kW to 5 MW

Medium

Distributed Generation

5 MW to 50 MW

Large

Distributed Generation

50 MW to 300 MW

Table 1.1: Distributed generation categorization [67].

power ratings or the generators size, factors that may vary for each appliance. After doing some research, it can be assumed that one of the most accepted definitions for distributed generation, is the one proposed by Professor Thomas Ackerman [67]: "Distributed generation is an electric power source connected directly to the distribution network or on the customer site of the meter." This definition of distributed generation does not define the rating of the generation source, as the maximum rating depends on the local distribution network conditions. However, it is advisable to categorize distributed generation systems according to the different power ratings, as shown in Table 1.1. In addition, the definition of distributed generation does neither define the size of the power delivery area, the penetration, the ownership nor the kind of operation of these kind of distributed resources. It cannot be assumed, as it is often done, that distributed generation stands for local power delivery, low system penetration, independent ownership and special operation. Professor Ackerman here proposes the differentiation between some common situations, which may also create confusion as there are not standardized definitions. For example, if the power output of distributed generation is used only within the local distribution network, he suggests the term embedded distributed generation. And if the distributed generation source is not centrally dispatched, he claims it should be called: not centrally dispatched distributed generation.

1.3

Self-Consumption Self-consumption is the name that defines those facilities in which electrical energy generation and consumption are simultaneously present, in such a way that the produced energy can be consumed near, or just at, the generation point. These kind of facilities can be isolated or connected to the electrical utility system. In case of being isolated, self-consumption facilities are not connected to the utility grid, since they are self-sufficient and do not need grid services. In contrast, self-consumption facilities can be connected to the utility grid to use it as back up, in a way that the facility is able to inject energy into the grid, or take it, depending on the consumption needs. This second case presents an important advantage with respect to the isolated facilities, and it is the guarantee of electrical energy supply in appropriate conditions all the time, aspect that is not possible in isolated

1.4 Power Reliability

9

systems. In this second scenario, the role of the distributor is crucial to guarantee a high reliability and power quality standard. New business models will be created for the distributor like for instance the ones based in the coordinated operation in this kind of facilities to guaranty the distribution network adequacy assessment. Since the employed technologies for self-consumption are usually intermittent and non-predictable (renewable energies such as wind or sun, that depend on weather conditions and as a result they have intrinsic characteristics that impede their dispatchability), the connection to a distribution grid is necessary to guarantee the service reliability. The inclusion of self-consumption facilities in the electrical system means a big change in the actual model, and it has many consequences at different levels. From a technical point of view, the the installation of self-consumption facilities may result in a reduction of grid losses, since the generation units are closer to the consumption points. On the other hand, self-consumption models disturb the configuration and the traditional modes of operation of the electrical system, since they mean the presence of new generation units at locations where there were only consumers. This fact implies the transformation of the centralized generation pattern and unidirectional power flows in the lines, into a new distributed generation scenario and bidirectional line power flows. This means a huge change in the electrical system, that could derive in harmful consequences for the system safety and reliability, if there is no previous adaptation of the electrical system, to assure the coordinated and compatible introduction of the new generation within the existing electrical utilities. As a result, the introduction and development of self-consumption should be accompanied by a rigorous study, that assures the establishment of this new model treatment and the quality and sustainability levels of the electrical system are maintained.

1.4

Power Reliability Power system reliability is defined as the overall ability of a power system to perform its function [7]. It is composed of two main aspects which are system security and system adequacy [50]. Security relates to the ability of the system to respond to dynamic or transient disturbances arising within the system like the abrupt loss of major generation or/and transmission facilities which can lead to dynamic, transient, or voltage instability of the system. On the other hand, adequacy relates to the existence of sufficient facilities within the system to satisfy the consumer load demand or system operational constraints. These includes the facilities necessary to generate, transport and distribute energy to the consumer load points. Therefore, adequacy is associated with static conditions which do not include system dynamic and transient disturbances.

10

Chapter 1. Introduction

It is important to mention that most of the probabilistic techniques presently available for reliability evaluation are in the domain of adequacy assessment. The ability to assess security is therefore very limited due to the complexity associated with modeling the system in this case [52]. This is why most of the indices currently used are adequacy indices and not overall reliability indices. 1.4.1

Functional Zones and Hierarchical Levels The basic techniques for adequacy assessment can be categorized in terms of their application to segments of a complete power system. These segments can be defined as the functional zones of generation, transmission, and distribution [50]. Figure 1.1 shows how these segments are combined to form the different hierarchical levels which are to be used in the adequacy assessment. Hierarchical Level 1 (HL1) in only concerned with the generation facilities. Hierarchical Level 2 (HL2) includes both generation and transmission facilities while HL3 includes all three functional zones in an assessment of consumer load point adequacy. In the present project we will focus in the adequacy assessment at the Hierarchical Level 1 as it is the one that satisfies our study’s needs. In a generation system (HL1) study, the total system generation is examined to determine its adequacy to meet the total system load requirements. The basic concern is to estimate the generating capacity required to satisfy the system demand and to have sufficient capacity to perform corrective and preventive maintenance on the generating facilities. The transmission system and its ability to move the generated energy to the consumer load points is ignored in generating system adequacy assessment. The basic modeling approach [53] for an HLl study is shown in Figure 1.2. Reliability studies, by the nature of the process that they represent, have a significant probabilistic content. There are two approaches to the solution of this probabilistic phenomenon: analytical and simulation methods. Analytical techniques [46] represent the system by analytical models and evaluate the indices from these models using mathematical solutions. Monte Carlo simulation methods, however, estimate the indices by simulating the actual process and random behavior of the system. This method, therefore, treats the problem as a series of experiments. There are merits and demerits in both methods [54], nevertheless, when complex operating conditions are involved and/or the number of severe events is relatively large, Monte Carlo methods are often preferable [50]. This is the main reason why we will use the Monte Carlo criteria along this project.

1.4.2

Monte Carlo Method Two main possibilities can be considered when Monte Carlo methods [50] are applied to power systems reliability evaluation. These methods are named as sequential and non-sequential techniques

1.4 Power Reliability

11

Generation facilities

Hierarchical Level 1

Transmission facilities

Hierarchical Level 2

Distribution facilities

Hierarchical Level 3

HL1

HL2

HL3

Figure 1.1: Hierarchical levels [50].

Load

Generation model

model

Risk model Figure 1.2: Conceptual tasks for HL1 evaluation [50].

[48][49]. The non-sequential technique, as well as the analytical approach, is usually restricted to the evaluation of expected values and, sometimes, to a limited range of system parameters. Hence, it is often required to know the likely range of the reliability indices, the probability of a certain value being exceeded, and similar parameters. These can only be assessed if the probability distribution function related to the index is previously known, and this is not often achieved with an analytical approach. In such situations, sequential simulation can be employed. In contrast, in sequential simulation, each subsequent system state sample is related to the previous set of system states. This results in a sequential time evolution of the system behaviour, which provides a wide range of reliability indices to be evaluated [63]. Sequential simulation is very useful when the historical evolution of the system partially determines the state of the system at any given time. It can be assumed that, nowadays, sequential simulation is the only realistic choice available to develop the distributions associated with the system index mean values [45]. 1.4.3

Methods in Reliability Evaluation A fundamental parameter in reliability evaluation is the mathematical expectation of a given reliability index. Salient features of the Monte Carlo method for reliability evaluation therefore can be discussed from an expectation point of view. Let Q denote the unavailability (failure probability) of a system and xi be a zero-one indicator

Chapter 1. Introduction

12 variable which states that xi = 0 if the system is in the up state xi = 1 if the system is in the down state The estimate of the system availability is given by: N 1 X ¯ Q= xi N i=1

(1.1)

where N is the number of system state samples. The unbiased sample variance is: N

V (x) =

1 X ¯ 2 (xi − Q) N − 1 i=1

(1.2)

When the sample size is large enough, equation (1.2) can be approximated by: N 1 X ¯ 2 V (x) = (xi − Q) N i=1

(1.3)

Because xi is a zero-one variable, it follows that: N X

x2i

i=1

=

N X

xi

(1.4)

i=1

Substituting equations (1.1) and (1.4) into equation (1.3) yields to: N N N 1 X 2 1 X 1 X ¯2 ¯ ¯−2·Q ¯2 + Q ¯2 = Q ¯−Q ¯2 V (x) = xi − 2 · xi Q + Q =Q N i=1 N i=1 N i=1

(1.5)

It is important to note that equation (1.1) gives only an estimate of the system unavailability. The uncertainty around the estimate can be measured by the variance of the expectation estimate: ¯ = 1 V (x) = 1 (Q ¯−Q ¯ 2) V (Q) N N

(1.6)

The accuracy level of Monte Carlo simulation can be expressed by the coefficient of variation, which is defined as: p ¯ V (Q) a= ¯ Q Substitution of equation (1.6) into equation (1.7) gives: s ¯ 1−Q a= ¯ NQ

(1.7)

(1.8)

Equation (1.8) can be rewritten as: N=

¯ 1−Q ¯ a2 Q

Equation (1.9) indicates two important points:

(1.9)

1.4 Power Reliability

13

1. For a desired accuracy level a, the required number of samples N depends on the system unavailability but is independent of the size of the system. Monte Carlo methods are therefore suited to large-scale system reliability evaluation. This is an important advantage of Monte Carlo methods compared to analytical enumeration techniques for reliability evaluation. 2. The unavailability (failure probability) in practical system reliability evaluation is usually much smaller than 1.0. Therefore, N=

1 ¯ a2 Q

(1.10)

This means that the number of samples N is approximately inversely proportional to the unavailability of the system. In other words, in the case of a very reliable system, a large number of samples is required to satisfy the given accuracy level. 1.4.4

Efficiency of Monte Carlo methods The same problem can be solved by different Monte Carlo techniques. These include different random number generation methods, different sampling approaches, and different variance reduction techniques, among others. It is thus sometimes required to compare the efficiency of different Monte Carlo methods. Suppose two Monte Carlo methods, which provide the same expectation estimates of the reliability index, are used to evaluate the same system. Let t1 and t2 denote computing times and σ12 and σ22 be the variances of the reliability index for the two methods, respectively. If the ratio η=

t1 σ12