Commercial Building Containing Generation Sources: A Technical and Economic Assessment and Its Potential to Participate in Demand Response Programs

The interest on studying the impact of demand response is growing, especially on residential and commercial buildings which are responsible for a considerable consumption of energy worldwide. Also, it is virtually unques-tionable that in most of these buildings there is a waste of energy, mainly electrical and thermal energy. In this context, the establishment of intelligent networks in these buildings, as well as the use of small or even medium-sized renewable sources of power can significantly contribute to the reduction and preservation of power. In this article, the results of the simulations carried out in a specific simulation program to evaluate the benefits brought by the in-stallation of some local sources of power on a commercial building are presented. It is evaluated the impact on some of the economic variables linked to that system as well as compared its greenhouse gas emissions for the conditions with and without the presence of the local generation. It will also evaluate the building’s response towards the utility requirements, mainly the pos-sibility to reduce or partially compensate the energy consumed, commonly referred to as Demand Response.


Introduction
Typically, demand response (DR) is referred to any program that motivates How to cite this paper: Del Carpio-Huayllas, T.E., Ramos, D.S. and Vasquez-Arnez, R.L. (2019) Commercial Building Containing Generation Sources: A Technical and Economic Assessment and Its Potential to Participate in Demand Response Programs. activated by infrared sensors to control and make an efficient use of water (water sinks) or to smartly operate the main doors of a building to preserve its inner temperature. Other aspects featuring a building as an intelligent one may be the maximum use of natural light instead of electric lamps, reuse of water, etc.
Automation systems such as BAS (Building Automation System), EMGI (Energy Management and Grid Interaction), and the building's information technology system interact each other through communication protocols that may have a centralized or decentralized arrangement.
The main scope of this article is to evaluate the benefits brought by the installation of some local sources of power and observe its impact on some of the economic variables linked to a commercial building, as well as compare its greenhouse gas emissions for the conditions with and without the presence of such a local generation. It will also be evaluated the building's response towards the utility requirements, commonly referred to as Demand Response. By applying tariffs with hourly, and/or contracts with financial incentives, the utility may induce changes in the load curve and improve operational bottlenecks. The data and costs presented next were obtained from commercial catalogues and information provided by some manufacturers. The energy and demand (i.e. peak and off-peak) unit tariffs were obtained from a local utility [19]. The currency used here corresponds to American dollars represented by the symbol ($).

Simulation of the Case under Analysis
The simulations were performed using the HOMER Legacy v2.68 beta program [20], [21]. In Cases 1, 2 and Case 3 (sensitivity analysis) the following variables were analysed: − Net Present Cost (NPC), which is a well-known method used to assess the viability of a certain project. Succinctly, it results of the difference between the investment value of an asset and the amount that will be redeemed at the end of the investment, brought to present value ($). These variables, together with the CO 2 emissions of each of the alternatives analysed are indicators on the building's viability to operate as a partially self-fed building. It will also be evaluated the energy generated by the renewable sources and the diesel generator to compute the cost that this energy represents if sold back to the grid.

Supply of Power Exclusively from the Utility (Case 1)
All the energy is exclusively supplied by the utility (see Table 1 Figure 3 corresponds to the simulated system presented in Figure 1. Note that the inclusion of the dc/dc converter linking the solar panel to the local generation (shown in Figure 1) is optional. In this case, it was not included.    Because the generation sources are relatively small in relation to the demand of the building, the utility will be supplying most of the demanded energy (blue columns). The diesel and wind generators (black and green colours respectively) contribute with less energy. The small PV solar generation (yellow colour), which practically does not appear in the graph, will also be contributing to supply power to the building. Table 2 shows how the generation of the different components behaves in cumulative terms. In this case, a greater contribution of power production by the utility, followed by the wind and diesel systems, can be observed. Although the diesel generator has a higher capacity than the wind turbine, the software gives priority to the contribution of the renewable sources.

Power Supplied by the Utility and the Sources within the Building (Case 2)
The energy produced by the local sources (E LS ) will be (sum of the first three rows shown in Table 2 The characteristics of each source during the power production is shown in Tables 3-6. For example, Table 3 shows that the PV array has the lowest capacity factor equal to 15.8% and it also has the least energy contribution to the load (4000 kW-h/yr). This is because its rated power has only 3 kW. The wind generation (Table 4) presents a better scenario with more than 52% of capacity factor and a much greater annual production (i.e. 688,000 kW/yr), although its rated capacity, compared to the PV array, is also greater (150 kW). Due to the issues above explained, mainly the generation cost, the diesel generator presents a low capacity factor (17.4%) with only 4745 hr/yr of operation.
The first column in Table 6 shows the global energy purchased from the utility, the second column represents the energy generated by the local sources which will be compensated (i.e. subtracted) from the first column. Thus, the commercial building will only pay the "net" energy purchased shown in the third column. It can also be observed the total (annual) energy and demand charges; the former calculated considering the net purchase of energy.        The "base case" condition is when the diesel price is equal to 0.873 US$/L and the wind speed equal to 7.51 m/s. Because these variables are global evaluation factors of the project, the NPC and LCOE variables will also be observed for these conditions.
In Table 7, a comparison of the main economic variables and the CO 2 emissions of both cases is presented. Note that Case 2 has got greater values than Case 1; this is due to the capital cost of the generation sources included to which it can be added the price of the fuel.

Sensitivity Analysis (Case 3)
The objective to perform a sensitivity analysis is that in situations where there are hypothetical variables, or variables prone to variations, there will be certain degree of uncertainty in the system, thus, a sensitivity analysis is needed.  fuel will not change over this period. There is obviously substantial uncertainty in considering this hypothesis, which might not reflect in a real manner the analysis performed and the project scenario.
The sensitivity analysis was performed for Case 2 (i.e. building fed by the utility and the local sources) for which two additional cases were considered: − Higher wind speed and lower diesel price.
− Lower wind speed and higher diesel price.

Sensitivity Considering a Higher Wind Speed and Lower Diesel Price
A drop in the diesel price to 0.75 $/L and an increase in the wind speed (8.5 m/s) was in this case applied. In Figure 6 and Figure 7 the behaviour of the base case is represented through the crossed blue lines inside each window. It can be observed that both the NPC and LCOE are highly sensitive to the fuel price (higher slope) and to a lesser extent to the wind speed.
In Table 8, the power production of the different components is shown. Note that an increase in the wind speed makes the wind generator to produce more energy.
The total power produced by the local sources will be: The quantities corresponding to the PV array did not change. This is because it has not been accounted in the sensitivity analysis. The reason is that its installed capacity is relatively small, thus, little change would produce in the whole power production.
The mean output power and the capacity factor of the wind generator are shown in Table 9. In Table 10, the main operative characteristics of the diesel generator are shown.
The energy purchased from the utility and the net energy sold by the building, as well as the demand and energy charges are presented in Table 11.
In this case, due to the lower fuel price the diesel generator operates at a daily basis (i.e. it is more used) operating approximately 50% (4745/8760) of the time along the year. It was not included the case when simultaneously the wind speed and the fuel price increase as the simulation toll would obviously give priority to the wind generator.

Sensitivity Considering Lower Wind Speed and Higher Diesel Price
Under this condition, a reduction of the wind speed to 6.5 m/s and an increase in the diesel price to 0.95 $/L, occurs. Analogously, the values of the NPC and LCOE relative to the best estimate are also shown in Figure 6 and Figure 7.
In Table 12, it can be observed that due to the increase in fuel price, the diesel generator reduces its power production in relation to the previous case.
The total power produced by the local sources will be ( Features like the mean output and the capacity factor of the wind generator are shown in Table 13. In Table 14, the main operative characteristics of the diesel generator are shown.
For this case, the energy purchased from the utility and the net energy sold by the building, as well as the demand and energy charges are presented in Table  15.
A synthesis of the total energy produced by the local sources presented in Table 2, Table 8 and Table 12, as well as the CO 2 emissions, is presented in Table  16.
The result of the sensitivity analysis considering a drop in the diesel price to 0.75 $/L and an increase in the wind speed (8.5 m/s) can also be seen in Figure 6 and Figure 7 (green lines). The analysis of these variations will be presented in the next section. Note that with the diesel price becoming higher (i.e. blue line shifted to the left side until it rides the red line) the value relative to the best estimate worsens (decreases) which increases the diesel NPC . A similar behaviour occurs in the case of the wind generation NPC.
Conversely, if the diesel price becomes lower (i.e. blue line is shifted to the right side until it rides the green line) the value relative to the best estimate rises  decreasing the diesel's NPC. This behaviour also occurs in the case of the wind generation NPC.
A similar behaviour occurs in the case of the LCOE in which the "best estimate value" decreases if the diesel price increases . Conversely, this value improves if the diesel price falls .

Commercial Building Participating in Demand Response Programs
This analysis was conducted considering some hypotheses, namely: If the energy generated by the local sources is injected into the grid by the This sell-back value represents approximately 48.4% of compensating profit in relation to the energy charge presented in Table 11.
2) Section 3.3.2 Analogously, in the second case of sensitivity (i.e. lower wind speed and higher diesel price) the sell-back value will be $ 150,396/yr, which represents approximately 33.66% of compensating profit in relation to the energy charge presented in Table 15. Note that the sell-back cost is more sensitive to the wind speed than to the diesel price.

Discussion
In the above example, the PV source is relatively small. This is because its size is limited by the available area of the building's roof. However, its minor contribution to the analysed costs, together with the contribution of the wind generators, made the difference in relation to Case 1.
Also, since the analysed building requires a relatively high degree of power availability and reliability (i.e. it can be the case of a shopping mall) a diesel genset was included. The impact of its acquisition and operational costs was partly offset by the renewable sources that have a null fuel cost.
In some countries, the application of the microgrid technology and the smart building technology is currently leveraged by some government incentives in the electric sector. This is the case of the Brazilian ProGD (Program for the Development of Distributed Generation) whose aim is to broaden and deepen the actions to stimulate the power generation by the consumers, as long as it is based on renewable sources, especially solar power. Another similar incentive in the country is PROINFA (Program for the Incentive of Alternative Sources) that

Conclusions
Moderate investments in generation sources in a building, in either renewable sources or those based on fossil fuels, may bring revenues proportional to the investment, which, in a medium term (i.e., a period less than the service life of the assets) can become significant.
The overall annual cost of the system (NPC) and LCOE is highly sensitive to the variation of the fuel price and, to a lesser degree, to wind speed variations.
Hence, investment in renewable generation sources would be more advantageous for this purpose.
The article shows in a straight manner the procedures and data required (for a specific simulation program) to assess the technical and economic impact of the sources contained in a building. Some features characterizing a building as smart, are described here. Along the research conducted, it was observed the lack of some general guidelines and standards to build up new buildings, or to convert conventional ones into smart structures, this should be the next challenge for the scientific community.