# MAXIMIZING LOCAL PV UTILIZATION USING SMALL-SCALE BATTERIES AND FLEXIBLE THERMAL LOADS

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4 or zero PV production, the battery discharges to cover the load reducing the electricity imports from the grid. In this setup, two storage options are available in the building: thermal storage and battery storage. Algorithm 1 (A1) assumes that only the thermal storage is present. Algorithm 2 (A2) considers the battery but does not use the thermal storage for PV self-consumption. Algorithms 3 and 4 consider both storage options but differ with respect to the priority with which they are charged when PV energy surpluses exist. Algorithm 3 (A3) prioritizes the HP, while Algorithm 4 (A4) stores energy first in the battery. The choice of the priority depends on efficiency values, the thermal energy demand of the building, the electricity consumption profile, and it might exhibit seasonal patterns. Since it is generally difficult to tell a priori which priority is the most appropriate, annual simulations can be used for this purpose. The proposed algorithms are compared against a base case (A0) where there is no battery and the HP is operated based on its internal controller. The operation principle of this controller is shown in Figure 2. The water temperatures at the seventh and tenth layer of the storage tan, T s7 and T s10, as well as the building temperature T b, are used as controller inputs. The respective set-points are denoted by T s7,min, T s10,max, T b,min, and T b,max. The HP internal temperature T HP is also used as a control input for reliability reasons, i.e. to allow switching off whenever a limit T HP,max is exceeded. In the following, the HP state is denoted by S HP and (t) denotes the current time step. According to Figure 2, the HP state changes when any of the above set-points is reached. In any other case, the HP state remains the same as in the previous time step. Note that the internal controller can also handle minimum operation time constraints; however, these are not shown in Figure 2 for the sae of simplicity. The control logic of algorithm A1 is shown in Figure 3. Denote by P PV the available PV power and by P l the total uncontrollable load. Similarly to A0, whenever any of the set-points T s7,min, T s10,max, T b,min, and T b,max is reached the HP turns on or off based on its internal controller. In any other case, the HP state is decided based on the net power P n, which is simply the power surplus in the building. In Figure 3, P HP,exp denotes the power that the HP is expected to consume if it turns on at the current time step. The parameter α є [0,1] determines which part of P HP,exp must be covered by P PV to allow HP operation. Higher α values will result in less frequent HP operation. Figure 3: Flowchart of algorithm A1. Figure 2: Flowchart of base case (algorithm A0) Figures 4-6 present the system operation under algorithm A2. The thermal storage is not utilized for PV energy management in this case; therefore, the HP operates based on A0. For this reason, to calculate P n the HP is treated as uncontrollable load. If power surplus (deficit) occurs and the battery is not fully charged (discharged), the charging (discharging) mode is enabled. If the KiBaM discharging or charging power constraints (8), (9) are active, any additional power is imported from or fed to the grid, respectively. If the SOC is 100% or below SOC min, then the battery idles. Algorithm 3 is described in Figure 7. Since the HP has priority over battery, the first part of A3 is identical to A1. After the HP state has been fixed, the battery is connected in charging or discharging mode depending on

5 P n. The same logic as before applies here for battery charging and discharging. Algorithms A1-A4, as well as the base case A0, can be expressed as a set of logic statements, which can be directly incorporated into Polysun via a component called programmable controller. This component allows logic statements to be programed in a high-level user friendly language. Also, specific operation intervals for thermal loads, or intervals during which operation is not allowed, can be defined using the same component. Figure 4: Flowchart of algorithm A2 Figure 6: Flowchart of battery control during discharging Figure 5: Flowchart of battery control during charging Algorithm 4, which is the most involved, is shown in Figure 8. The discharging phase is identical to A3; however, the charging priority is reversed. In presence of a power surplus, the battery is charged first. If the charging power constraint (9) is active or the battery is fully charged, the remaining power is consumed by the HP, provided that it is more than expected power consumption of the HP; otherwise, it is fed to the grid. Figure 7: Flowchart of algorithm A3

7 Note that in these simulations the battery capacity is not optimized; this will be investigated in Section Annual results Tables II-VI present annual results for energy imports (exports) from (to) the grid, HP and total electricity consumption, battery charging and discharging energy (for algorithms A2, A3, and A4), PV self-consumption ratio, and the resulting electricity bill. Table II: Simulation results for Base Case (A0) Imports from grid (Wh) Exports to grid (Wh) HP consumption (Wh) Total consumption (Wh) PV self-consumption ratio (%) Electricity bill ( ) Table III: Simulation results for algorithm A1 Imports from grid (Wh) Exports to grid (Wh) HP consumption (Wh) Total consumption (Wh) PV self-consumption ratio (%) Electricity bill ( ) Table IV: Simulation results for algorithm A2 Imports from grid (Wh) Exports to grid (Wh) HP consumption (Wh) Total consumption (Wh) Battery charging (Wh) Battery discharging (Wh) PV self-consumption ratio (%) Electricity bill ( ) Table V: Simulation results for algorithm A3 Imports from grid (Wh) Exports to grid (Wh) HP consumption (Wh) Total consumption (Wh) Battery charging (Wh) Battery discharging (Wh) PV self-consumption ratio (%) Electricity bill ( ) Table VI: Simulation results for algorithm A4 Imports from grid (Wh) Exports to grid (Wh) HP consumption (Wh) Total consumption (Wh) Battery charging (Wh) Battery discharging (Wh) PV self-consumption ratio (%) Electricity bill ( ) Using only the HP and the building thermal inertia in A1 increases the PV self-consumption by approximately 1.5% compared to A0. On the other hand, the battery itself (A2) demonstrates a much higher potential and leads to PV self-consumption increase of roughly 15.5%. When both HP and battery are available, marginally higher self-consumption ratios are achieved when the priority during charging is given to the HP. Algorithm A3 leads to the highest PV self-consumption ratio, which is equal to 36.46%, i.e. more than one third of the available PV energy is consumed within the building premises. A3 gives the best results also in terms of savings for building owners, which amount to roughly 85 per year. Generally, increasing the PV self-consumption reduces the electricity bill. However, this is not the case for A1. Although A1 self-consumes more PV energy than A0, it results in marginally higher costs. This is because A1 operates the system in a less energy efficient way, increases the thermal losses, and eventually requires more electric energy for the HP. Usually this energy is imported from the grid, which brings additional costs. Clearly, the battery alone achieves most of the existing potential for PV self-consumption maximization. Comparing Tables IV and V, one can see that introducing also the HP in the control scheme further reduces the annual cost only by 6. However, this reduction comes at virtually zero cost, since the marginal cost of integrating the HP in the already installed controller is negligible. Similarly to A1, shifting the HP demand in A3 increases thermal losses and the annual HP consumption. Nevertheless, these additional losses are covered by battery discharge, rather energy imports from the grid. This explains why A3 achieves a lower cost than A2, whereas A1 increases the cost compared to A Daily operation results Figures show operation results from typical days in winter, spring, summer and autumn for A3, which demonstrates the best performance over the year. We focus on PV production, UL and HP consumption, battery charge and discharge energy, building temperature, battery SOC, and HP internal temperature. Figure 10: Production and consumption of system components during two typical days in winter (January) Figure 11: Building temperature, HP temperature, and

8 battery SOC during two typical days in winter (January) according to constraint (9). Similar observations can be Figure 12: Production and consumption of system components during two typical days in spring (April) Figure 14: Production and consumption of system components during two typical days in summer (July) Figure 13: Building temperature, HP temperature, and battery SOC during two typical days in spring (April) The seasonal patterns of HP operation can be clearly seen in the figures. The HP runs most of the time during a cold winter day, whereas it turns on only a few times during a summer day. In winter when PV production is low, HP operation is determined by the building heat demand, i.e. the HP consumes the minimum amount of energy that is required to eep building temperature right above 21 o C. Thus, the potential for PV energy management via HP control is very limited. In summer, the PV production is high; however, only a small part of the PV energy surplus is stored as thermal energy. This is because prolonged HP operation leads to overheating due to the low building heat demand. This can be seen in Figure 15, where the HP temperature remains often at or above T HP,max. This is in contrast to Figure 11, where the HP temperature in winter is significantly lower on average. Our simulations indicate that the potential for PV self-consumption maximization via HP control is higher in spring, and to a lesser extend in autumn. For example, the HP can effectively absorb the excess PV power during the first day of Figure 12, because high PV production coincides with increased heat demand and low HP temperatures (see Figure 13). If PV power surpluses exist during day-time, the battery exhibits the expected pattern: it charges during day-time and discharges in the evening and night hours to cover the load. Note that charging cycles might be interrupted by smaller discharging cycles in case the HP turns on and the PV power is not sufficient to provide the required power. In all cases, the rate of SOC increase reduces when the battery is nearly fully charged, Figure 15: Building temperature, HP temperature, and battery SOC during two typical days in summer (July) Figure 16: Production and consumption of system components during two typical days in autumn (October) Figure 17: Building temperature, HP temperature, and

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