Baseline and optimal energy costs
The daily energy and cost savings are presented in Table 4.2. Baseline cost is the bill which the hotel pays in the case study before the OC intervention while the optimal cost is the bill after OC intervention.
This bill is for the grid energy consumed by the domestic load, HPWH and battery charging. The PV energy sales is the generated revenue from sales to the grid.
The energy saved, on the other hand, represents the grid power not delivered (energy not served) to the load, which would have been delivered without the OC intervention of this model. This energy is the difference between the baseline and the energy saved owing to the OC strategy. This energy not served directly translates into a relief on the utility company’s primary input (e.g gas, coal, biomass, nuclear energy, water) in power generation. The reduction in primary energy input helps to mitigate climate change and greenhouse gases emission. However, these impacts are not considered in this chapter.
Heat pump water heater
The heat pump model is developed according to , with a fixed power demand Php rating, operating at full capacity. The temperature distribution of the hot water is assumed to be uniform in a constant water volume, neglecting stratification. For modeling simplicity, the energy losses in the evaporator, refrigerant and compressor are neglected; however, the efficiency of the electric motor driving the compressor is around 96%. The overall efficiency of other thermal components of the HPWH is accounted for by COP in the case study. In order to minimize the complexity of this modeling problem, a constant COP is assumed as well, though in practice it varies. Therefore, only energy losses due to hot water demand QD(t) and convectional (standby) loss QL(t) are modeled in this chapter. The standby losses, QL(t), are thermal losses dispatched through the tank’s casing material. These losses can be minimised through increased thermal insulation and application of low thermal conductivity materials.
The PV power generation Ppv(t) are input data in this model. A power source from zero to its maximum rated measured value is taken from the case study of our previous research [23, 32]. The PV power input data are given in Figure 5.5. The PV supplies Ppv(t) to the HPWH and the excess is sold to the grid at the prevailing feed-in tariff. This model excludes the energy storage system in order to lower the initial investment cost, which hampers the implementation of these systems at household level. The grid acts as the DREs’ energy storage system.
The case study is based on a farmhouse situated in the coastal town of Port Elizabeth in South Africa.
The main intervention of this model proposes an OC and renewable power integration solution to the HPWH installed at this farmhouse. The current (baseline) situation is that the HPWH installed at the farmhouse is controlled by the digital thermostat and supplied by the grid alone. Despite this being the normal mode of control/operation, it is far from optimal from a daily operational point of view.
Because of the operation constraints of the HPWH, the sampling time ts is taken to be 30 minutes, giving the total sampling interval N = 48 per 24-h horizon. The 24-h daily simulation has been mentioned earlier in section 5.1, because of several other factors such as lack of annual consolidated water demand and DREs output data in South Africa . It is expected that the results of a day’s performance may vary if modeled on an annual basis, because of variations in input data such wind power, PV power, and inlet water temperature over seasons. In this chapter, a typical winter day is assumed because of its relatively higher hot water and energy demand, to simulate the worst case scenario. There are errors an a 24-h model’s performance resulting from a variation of DREs and other input data over the season. However, these can be minimized by application of MPC in future research, as recommended in section 5.5. Since cost minimization is the objective, for precise economic and payback (break-even) period estimation, simulations are done on each selected day in all seasons: summer, autumn, winter and spring in the case study.
CHAPTER 1 INTRODUCTION
1.1 RESEARCH BACKGROUND AND MOTIVATION
1.2 OBJECTIVES OF THE THESIS
1.3 LAYOUT OF THE THESIS
CHAPTER 2 LITERATURE STUDY
2.2 ENERGY EFFICIENCY UTILIZATION
2.3 HEAT PUMP WATER HEATERS
2.4 RENEWABLE ENERGY HYBRID SYSTEMS
2.5 OPTIMIZATION AND CONTROL METHODS IN ENERGY SYSTEMS
CHAPTER 3 OPTIMAL POWER DISPATCH OF A GRID TIED-BATTERYPHOTOVOLTAIC SYSTEM SUPPLYING HEAT PUMP WATER HEATERS
3.2 MODEL DEVELOPMENT
3.3 SIMULATION RESULTS AND DISCUSSION
CHAPTER 4 OPTIMAL ENERGY CONTROL OF GRID TIED PV-DIESELBATTERY HYBRID SYSTEM POWERING HEAT PUMP WATER HEATER
4.2 MODEL DEVELOPMENT
4.3 SIMULATION RESULTS AND DISCUSSION
CHAPTER 5 OPTIMAL CONTROL OF A WIND-PV-HYBRID POWERED HEAT PUMPWATER HEATER
5.2 MODEL DEVELOPMENT
5.3 DISCRETE MODEL FORMULATION
5.4 SIMULATION RESULTS AND DISCUSSION
CHAPTER 6 OPTIMAL CONTROL OF A FUEL CELL/WIND/PV/GRID HYBRID SYSTEM WITH THERMAL HEAT PUMP LOAD
6.2 MATHEMATICAL MODEL FORMULATION
6.3 DISCRETE FORMULATION OF THE MODEL
6.4 SIMULATION RESULTS AND DISCUSSION
CHAPTER 7 COMPARISON OF LIFE CYCLE COST ANALYSIS
7.2 LCC OF CHAPTER 3 MODEL
7.3 LCC OF CHAPTER 4 MODEL
7.4 LCC OF CHAPTER 5 MODEL
7.5 LCC OF CHAPTER 6 MODEL
7.6 LCC SUMMARY
CHAPTER 8 CONCLUSION
8.2 RECOMMENDATION AND FUTURE RESEARCH