Technical and Economic Assessment of Solar Photovoltaic and Energy Storage Options for Zero Energy Residential Buildings
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1 Technical and Economic Assessment of Solar Photovoltaic and Energy Storage Options Pedro Moura, Diogo Monteiro, André Assunção, Filomeno Vieira, Aníbal de Almeida Presented by Pedro Moura
2 Introduction There is an increasing penetration of PV in buildings. Its generation profile presents significant temporal variability and cannot be reliably dispatched or perfectly forecasted. In the context of residential buildings, the generation (usually PV) and consumption do not have the same variation profile, leading to: High power flows between the household and the grid. Impact on the grid management and on the cost-effectiveness of the PV generation system.
3 Introduction Several residential appliances can be used as a Demand Response (DR) resource: Washing and drying appliances can be rescheduled to periods of higher energy generation from renewable sources. Its impact for achieving a high self-consumption level is very limited (about 10%). Energy storage can store the surplus of generation to be used later in the periods with high consumption and small or null generation. The cost of the storage technologies is decreasing, and soon it is expected to become economically suitable for small applications. Simultaneously, in EVs, the need of periodic replacement of the battery will lead to a large number of batteries available in the upcoming years.
4 Objectives For the average Portuguese residential building, the achieved self-consumption level, as well as the associated economic impacts were assessed for different sizes of PV systems,. To determine the optimum installed PV power for buildings without energy storage. The impact of energy storage is assessed for a zero energy building, considering lithium-ion batteries with different sizes: New batteries and repurposed batteries from EVs. Different solutions were assessed regarding its impact on self-consumption level. Cost-effectiveness assessed, considering the actual and future costs of storage technologies. To determine the optimum size of energy storage.
5 PV Generation Sizing It was considered the average consumption of electricity per household in Portugal (3.67 MWh/year). Consumption profile from the REMODECE project. PV system sized (simulated in PVSyst) to ensure such consumption, considering the solar radiation in Coimbra. 2.4 kw (10 Panels of 240 Wp). 10 Panels of 240 Wp Additional scenarios to ensure 75%, 50% and 25% of the average electricity consumption. Scenario 100% 75% 50% 25% Power (kw) Generation (kwh/year)
6 Energy Storage Sizing Effective energy storage capacity of 60% of the average daily consumption (6 kwh). Minimum SoC of 30% (8.57 kwh); Lithium-ion batteries - efficiency of 92% (9.32 kwh); The default value in the market is 200 Ah (10.2 kwh). 200 Ah 48 V (51.2 V) Additional scenarios to ensure 100%, 80%, 30% and 15% of the average daily consumption. Scenario 100% 80% 60% 30% 15% Required Capacity (kwh) Standard Capacity (kwh)
7 Energy Storage Sizing For the repurposed batteries from EVs, a degradation up to 70% of the initial capacity was considered. Electric Vehicle All the presented options, and their respective capacity for second life applications can fulfil the storage needs of an average Portuguese household. For further analysis, the battery of a Nissan Leaf, due to its larger market share, and the battery of a Citroen C0, due to its smaller capacity and yet capable of offering the storage needs of a Portuguese household, were considered. Initial Capacity (kwh) 2 nd Life Capacity (kwh) Tesla Model S Nissan Leaf BMW i Chevy Volt Citroen C
8 PV Generation and Energy Storage System PV array connected to the DC bus by a boost converter with a duty cycle controlled to ensure the MPPT (Maximum Power Point Tracking) using an Incremental Conductance algorithm. Lithium-ion battery charging and discharging ensured by the bidirectional DC-DC converter (buck mode during the charging process and boost mode during the discharging). PV Panel DC Bus DC-DC Converter Bidirectional Inverter Loads Grid Battery Bidirectional DC-DC Converter The system was modelled in MATLAB/Simulink and used to simulate the system operation.
9 PV Generation and Energy Storage System Minimization of Power Flows between the household and the grid: Only the energy generated by the PV system is stored. Priorities of the energy storage optimization: Battery Generation > Demand Generation < Demand SoC = 30% 1. Available generation to loads 1. Needed generation to loads 2. Remainder energy need 2. Remainder generation to received from grid storage 30% < SoC < 100% 1. Available generation to loads SoC = 100% 2. Available stored energy to 1. Needed generation to loads loads 2. Remainder generation to grid 3. Remainder energy need received from grid Minimization of costs: When energy has to be consumed from the grid, such energy is only consumed in off-peak periods and stored.
10 Self-Consumption PV Generation The system was simulated for a typical residential household in Coimbra, during one year, using real data of solar radiation and electricity consumption. The 4 scenarios of PV sizing were simulated, as well as a baseline scenario without generation. With 2.4 kw of PV power, 58.9% of the generated energy has to be injected into the grid (self-consumption of 41.1%). Scenario 100% 75% 50% 25% 0% Power (kw) Gene (kwh/y) H2G (kwh/y) G2H Onpeak G2H Offpeak G2H (kwh/y) Total (kwh/y) Decreasing the size of the PV system, leads to an increase on the self-consumption.
11 Self-Consumption Energy Storage The system was then simulated for a PV generation of 2.4 kw (able to ensure 100% of the electricity consumption in average year) and considering the five scenarios of energy storage. In different days, there are periods with energy consumed from the grid (negative grid power) and other periods with energy injected into the grid (positive grid power).
12 Self-Consumption Energy Storage In winter months, energy is consumed from the grid and in summer energy is injected into the grid. For the smaller batteries, there are injection of energy and consumption during the same month. By decreasing the battery size there is an increase on the energy injected and consumed.
13 Self-Consumption Energy Storage With a 16.6 kwh battery, 12.4% of the generated energy is injected into the grid and 12.4% of the consumed energy imported from the grid, leading to a selfconsumption of 87.6%. Decreasing the size of the battery leads to a decrease on the self-consumption. Scenario 100% 80% 60% 30% 15% Cap. (kwh) H2G (kwh/y) G2H Onpeak G2H Offpeak G2H (kwh/y) Total (kwh/y) The total exchange (H2G + G2H) increases for smaller batteries. The smaller batteries do not have enough capacity to avoid the consumption of energy during on-peak hours.
14 Self-Consumption Energy Storage The system was also simulated with a PV generation of 2.4 kw, but considering the two scenarios of repurposed batteries. With a repurposed battery with 16.8 kwh of second life capacity, only 12.4% of the generated energy is injected into the grid and 12% imported from the grid, leading to a self-consumption of 87.6%. Decreasing the size of the energy storage system, leads to a decrease on the self-consumption. Scenario Large Reused Battery Small Reused Battery Capacity (kwh) H2G (kwh/year) G2H (kwh/year) Total (kwh/year)
15 Economic Assessment A typical Portuguese household with an installed power of 6.9 kva (in normal low voltage) and a time-of-use tariff with two periods was considered. The Portuguese regulation for self-consumption allows for a paid price of the energy injected to the grid of 90% of the average monthly price of the Portuguese spot electricity market. The economic assessment was done by calculating the Net Present Value and the payback. The benefit was calculated through the difference in the yearly energy cost between the selected and the reference scenarios; Discount rate of 5%; Grid to House House to Grid Off-Peak (10 p.m. - 8 a.m.) On-Peak (8 a.m. -10 p.m.) /kwh /kwh /kwh Lifetime of 30 years for PV panels and 12 years for batteries.
16 Economic Assessment - PV Generation Lower yearly energy costs with larger PV systems. Higher system costs with larger PV systems. All systems are cost-effective (positive NPV). Smaller PV systems have a shorter payback. Shorter payback with a 0.6 kw PV system (25%). Higher NPV with a 1.2 kw PV system (50%).
17 Economic Assessment Energy Storage Costs for the energy storage systems in 2017 were estimated considering a battery cost of 480 /kwh and an additional cost of 15% for the BMS. Total cost of about 550 /kwh. Only the two smaller batteries (2.4 and 5.12 kwh) are cost-effective. Shorter payback with a 2.4 kwh battery. Higher NPV with a 5.1 kwh battery.
18 Economic Assessment Energy Storage Forecasted costs for 2020 were estimated considering a battery cost of 175 /kwh and an additional cost of 15% for the BMS. Total cost of about 200 /kwh The 10.2 kwh battery also becomes costeffective. The best solution is the 5.1 kwh battery Slightly higher payback Much larger NPV Higher technical impact
19 Economic Assessment Energy Storage Cost of 34 /kwh for repurposed batteries Additional costs with the BMS, encapsulation and installation. Scenario 16.8 kwh Reus kwh Reus. Batter y ( ) BMS ( ) Other s ( ) Total ( ) Cost of 34 /kwh for repurposed batteries. Both batteries are cost-effective. The best solution is the 10.2 kwh battery Shorter payback. Larger NPV.
20 Conclusions Self-consumption: With a 2.4 kw PV system the self-consumption level is only 41.4%. With a 10.2 kwh battery the self-consumption level is 86.4%. Cost-Effectiveness : All PV systems are cost-effective. With the costs in 2017, only the 2.4 and 5.1 kwh batteries are cost-effective. With the costs for 2020, the 10.2 kwh battery also becomes cost-effective. With the repurposed batteries the two systems are cost-effective. Best economic indicators: 1.2 kw PV system. 5.1 kwh battery kwh repurposed battery.
21 Technical and Economic Assessment of Solar Photovoltaic and Energy Storage Options Pedro Moura, Diogo Monteiro, André Assunção, Filomeno Vieira, Aníbal de Almeida Presented by Pedro Moura
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