**3.1 Design step**

*Green Energy Advances*

**Figure 3.**

**Incentive policies**

Renewable energy targets

Feed-in tariff/ premium payment

Electric utility quota obligation/ RPS

Net metering

Tradable REC

Capital subsidy, grant, or rebate

Reductions in sales, energy, CO2, VAT, or other taxes

Public investment, loans, or grants

*(\*indicates state/provincial).*

*Promotion policies of renewable energy in several countries [21].*

*World map of more than 360 internationally known net-zero energy buildings [20].*

**Australia Belgium China France Germany Italy Japan Spain The** 

O O R R O O R O O R\*

• R R R R R O R\*

O • O O O R\*

O O O O O O O •

O • O O O O O O O O

O O O O O O O O

*O, existing national (may also include state/provincial);* •*, existing subregional (e.g., state/provincial); R, revised* 

• O O O R\*

O O O O O O O

**United Kingdom** **The United States**

**42**

**Table 1.**

Although no exact approach has been developed for the target of zero balance during the design phase of ZEB, there are still some consensus and several common design elements for designing ZEB. A thorough design approach was proposed which involves 12 steps containing four foundational procedures, that is, applied metrics (e.g., primary energy, the cost), passive design (e.g., building envelope, orientation), active design (e.g., HVAC, lighting), and renewable energy system design (e.g., photovoltaic panel, wind turbine) for the design of ZEB [22, 23], as shown in **Figure 4**. Theoretically, design optimization of ZEB should be conducted considering the three vital design steps, that is, steps 7, 8, and 9, simultaneously to obtain a comprehensive combined design option for ZEB. Therefore, design optimization for ZEB is usually solved by integration of two or more software.

Passive design is an important procedure to reduce the building energy demand as much as possible. Then, high-efficiency active energy systems such as heating, cooling, and ventilation systems and lighting systems should be applied and improved together with high-performance control strategies; these could further reduce operational energy consumption in the building. Lastly, the feasibility of renewable energy technology should be assessed and selected as an on-site power supply system which works together with the power grid to reach the target of zero energy demand.

Various software tools have been developed to facilitate the selection of passive design, active design, and RES for buildings; several popular software are listed and compared in **Tables 2** and **3**. In ZEB design, the building energy demand can be firstly evaluated by using building energy simulation software such as EnergyPlus or TRNSYS. The selection of suitable renewable energy system for the building can then be conducted in software such as HOMER and TRNSYS. The design optimization software, HOMER, is developed by the US National Renewable Energy Laboratory (NREL) to assist in design optimization of hybrid energy systems for both grid-connected and autonomous building based on net present cost [24–27]. However, HOMER can only address a single-objective function for minimizing the net present cost, and it cannot solve multi-objective problems [28].

### **3.2 Performance evaluation criteria**

It is important to determine the evaluation criteria at the design stage. Various criteria have been proposed from a different perspective of users, which can be classified into four aspects covering technical and environmental issues, economic factors, and the interaction between building and grid, as shown in **Figure 5**.

#### **Figure 4.**

*The main steps to designing nZEB.*


**45**

design, RES) for ZEB.

*Definition and Design of Zero Energy Buildings DOI: http://dx.doi.org/10.5772/intechopen.80708*

Economical analysis

Technical analysis

Storage device

Thermal system

**Table 3.**

**Figure 5.**

Bioenergy √

Advantage User-friendly, easy

to understand, efficient graphical representation of results, hourly datahandling capacity

*Comparison of renewable energy simulation software [30].*

*The main four factors for evaluating ZEB performance.*

In terms of technological factors, recent researches have been focused on feasibility and/or reliability study of different technologies (passive design, active

for the building to achieve zero energy building for a particular region.

value, especially the cost and its payback period of installing on-site RES.

• Feasibility: Available technologies should be assessed to identify the possibility

**HOMER HYBRID2 iHOGA RETScreen**

√ √ √ √

√ √ √ √

√ √ √ √

PV system √ √ √ √ WT system √ √ √ √

√

User-friendly, multiple electrical load options, detailed dispatching option

multi- or mono-objective optimization, sensitivity analysis, low computational

User-friendly, strong product database, financial analysis

time, net balance

Generator set √ √ √

Hydro energy √ √

• Reliability: The criterion estimates the ability of the selected combined technologies for the building to perform its required function for a specified time.

In terms of economic factors, ZEB users are more concerned about the economic

#### **Table 2.**

*Comparison of building energy consumption software [29].*


*Definition and Design of Zero Energy Buildings DOI: http://dx.doi.org/10.5772/intechopen.80708*

#### **Table 3.**

*Green Energy Advances*

**44**

**Figure 4.**

*The main steps to designing nZEB.*

Room heat balance calculation

Nature ventilation calculation

Connection with other

*Comparison of building energy consumption software [29].*

software

**Table 2.**

**DOE-2 eQUEST EnergyPlus ESP-r DeST TRNSYS**

Humidity calculation √ √ √ √ √ Heat comfort calculation √ √ √ √

Sunlight analysis √ √ √ √ √ √ Greenhouse gas √ √ √ √ √ √

√ √ √ √ √

√ √ √ √ √

√ √ √ √

*Comparison of renewable energy simulation software [30].*

#### **Figure 5.**

*The main four factors for evaluating ZEB performance.*

In terms of technological factors, recent researches have been focused on feasibility and/or reliability study of different technologies (passive design, active design, RES) for ZEB.


In terms of economic factors, ZEB users are more concerned about the economic value, especially the cost and its payback period of installing on-site RES.

• Economic value [life cycle analysis (LCA), net present cost (NPC)]: The proposed renewable energy alternative will be assessed using one of the engineering economic techniques which are net present cost (NPC), life cycle analysis (LCA), benefit/cost analysis, and payback period.

In terms of environmental factors, the reduction of building load will definitely reduce the energy required from the grid and on-site RES size, which can be measured as pollutant emission.

• Pollutant emission: The criterion measures the equivalent emission of CO2, air emissions which are the results of applying different technologies in ZEB for a particular period.

In terms of grid interaction factors, the two-way electricity flow between building and grid poses more than technological challenges; those ZEB homeowners may make heavier use of the grid than the conventional building under one-way power flow. Grid interaction index is one of the indicators used to assess the grid stress caused by ZEB.

• Grid interaction index (GII): The criterion is defined as the standard deviation of the building-grid interaction over the specified time (e.g., 1 year). It is used to estimate the average stress of building on the grid, and a low standard deviation is preferred [28].

#### **3.3 Optimization method**

#### *3.3.1 Single-objective design optimization*

It is reported that there are more than 50% of design optimization problems that are addressed as single-objective optimization problems, and they are usually focused on the most important criteria such as economic cost or environmental issues. For designing ZEB, the optimization may be conducted by focusing on the only one aspect of ZEB performance, e.g., NPC and CO2 emissions. Besides, since multi-objective design optimization problems can also be transformed into singleobjective optimization problems by using weighting factor, it is reasonable to convert all of the concerned ZEB performance indices into one combined function, as shown in (Eq. (3)). Where X represents a vector of design variables at the design stage, fave and fi (i = 1, 2…n) are the combined objective and the normalized sub-objectives, respectively; wi is the corresponding weighting factor for each sub-objective:

$$\text{Min}\,f\_{an\epsilon} = \,^\|w\_1 \times f\_1(\mathbf{X}) \star w\_2 \times f\_2(\mathbf{X}) \star \dots \star w\_n \times f\_n(\mathbf{X})\tag{3}$$

$$\text{s.t.} AX \le a \tag{4}$$

$$g\_1(X) \quad \succeq 0 \tag{5}$$

$$\lg\_2(X) \tag{6}$$

**47**

**Figure 6.**

*Definition and Design of Zero Energy Buildings DOI: http://dx.doi.org/10.5772/intechopen.80708*

**3.4 Penalty cost for ZEB**

designed by designers.

*PC* =

**3.5 Individual ZEB or ZEB cluster**

optimization (PSO) is another favored method for optimal design of energy systems in

Although the progressive incentive policies have been recognized to widely encourage the installation of renewable energy system for buildings, the financial support scheme is forecasted to be downtrend and RES cost to be high, which are a barrier for promoting future buildings to be zero energy buildings. Therefore, a penalty cost scheme may be a good solution to build up the public's confidence and

A comparison of the building cost under different mismatch ratios is shown in **Figure 7**. It is found that the minimum total cost is supposed to be located in O1 under mismatch ratio less than 0, possibly between −1.0 and 0.0, indicating that the selection of design option under mismatch ratio of 1.0 is not cost-effective. However, the introduction of penalty cost can move the minimum cost from O1 to O2, or the higher mismatch ratio the less cost, depending on the type of penalty cost

The total cost (*TC*) of the building basically consists of the initial cost (*IC*) of RES (e.g., PV, WT) and the operation cost (*OC*) during the building usage stage due to the electricity consumption from grid and oil consumption (Eq. (4)). The penalty cost can be expressed as a mathematic expression, which is assumed to follow a segmented function, as shown in Eq. (5). It should be noted that the cost

*TCP* = *IC* + *OC* + *PC* (7)

*TC*1.0 × (*a* − α × *SF*),*SF* < *p*<sup>1</sup>

In recent years, wide ranges of researches are available on the topic of RES design/control for an off-grid building or a village in a remote area [25–27].

*TC*1.0 × (*c* − δ × *SF*),*SF* ≥ *p*<sup>2</sup>

*TC*1.0 <sup>×</sup> (*<sup>b</sup>* <sup>−</sup> <sup>β</sup> <sup>×</sup> *SF*),*p*<sup>1</sup> <sup>≤</sup> *SF* < *<sup>p</sup>*2

. (8)

results may be greatly different according to the designed penalty cost:

⎧ ⎪ ⎨ ⎪ ⎩

*Single-/multi-objective design optimization using GA/NSGA-II.*

recent papers [33, 34]. A typical optimization process is shown in **Figure 6**.

encourage them to be actively involved in ZEB application.

#### *3.3.2 Multi-objective design optimization*

The design and operation of ZEB are actually integrated with building owners, environment, energy source, and smart grid; it is, therefore, a multi-objective design optimization problem with even contradicting objectives. In general, genetic algorithm (GA) is the most popular optimization approach for single-objective and multi-objective optimizations of energy systems in numerous studies [31, 32]. Besides, particle swarm

optimization (PSO) is another favored method for optimal design of energy systems in recent papers [33, 34]. A typical optimization process is shown in **Figure 6**.
