Battery Dispatch Research Articles

Assessment of time resolution impact on the modeling of a hybrid CSP-PV plant: A case of study in Chile

This study analyzes the time resolution impact on the modeling of a hybrid CSP-PV plant integrated with thermal storage and a battery bank for two locations in Chile. Daily and annual results were evaluated varying the time resolution from 1, 5, 10, 15, 30 to 60 min, as well as the capacity factor and LCOE. In this way, the CSP plant was the component most affected by the time resolution, followed by the battery dispatch, while the effect on PV production estimation was marginal. Control procedures of the receiver and power block were more realistically captured with time steps between 1 and 5 min, while with higher time steps, variability effects were neglected. Annual and techno-economic results indicate an overestimation of the total yearly production of the hybrid plant as the time step is increased, leading to an underestimation of the LCOE. Variations in the capacity factor and LCOE were around ±2–3% in both locations using the 5-minute time step (with respect to the 1-min results), and between ±4–6% using 10–60 min time steps. Different cases of study varying the dispatch strategy and components sizing were evaluated, showing that time resolution impact is lower for oversized PV configurations with respect to the CSP plant, and higher when the CSP plant is oversized with respect to the PV, regardless the dispatch strategy. Finally, this work provides some recommendations analyzing the advantages and drawbacks of implementing different time steps at each stage in the development of a hybrid solar power plant.

Concurrent optimization of thermal and electric storage in commercial buildings to reduce operating cost and demand peaks under time-of-use tariffs

Demand-side management (DSM) in response to market-based electricity tariffs can potentially increase the efficiency and reliability of the electric power grid. This study introduces a novel, one-day-ahead DSM framework which optimizes temperature setpoints and battery dispatch in office buildings, subject to a time-varying and/or demand-based electricity tariff. To reflect real world implementation, our framework operates in two-steps. First, during the passive, battery-only DSM optimization, historical weather and electricity load data for a given building are used to determine its optimal battery capacity. Second, once the battery has been installed, a one-day-ahead, real-time DSM algorithm optimizes both the building’s daily temperature setpoints and the battery's charge/discharge pattern. The optimization objective is to minimize the total operating cost (tariff charges and battery system) while still satisfying occupants’ thermal comfort. Using a case study with a medium-fidelity electric load model for a standard office building, the performance of the proposed framework is validated by quantifying savings in operating cost, reduction of monthly grid peak loads, and the achieved human occupant comfort. To illustrate the advantage of optimizing temperature setpoints and battery dispatch concurrently, the combined performance is compared with that achieved by standalone DSM (i.e., using only battery dispatch or only temperature setpoints). We found that concurrent optimization can reduce a building's monthly peak demand on the grid by up to 26%. Electricity tariff charges are reduced by 11%, more than is required to offset storage costs, thus providing an overall profit to building operators who use such DSM. Payback time is approximately 5 years.

Ageing and Efficiency Aware Battery Dispatch for Arbitrage Markets Using Mixed Integer Linear Programming †

To achieve maximum profit by dispatching a battery storage system in an arbitrage operation, multiple factors must be considered. While revenue from the application is determined by the time variability of the electricity cost, the profit will be lowered by costs resulting from energy efficiency losses, as well as by battery degradation. In this paper, an optimal dispatch strategy is proposed for storage systems trading on energy arbitrage markets. The dispatch is based on a computationally-efficient implementation of a mixed-integer linear programming method, with a cost function that includes variable-energy conversion losses and a cycle-induced battery capacity fade. The parametrisation of these non-linear functions is backed by in-house laboratory tests. A detailed analysis of the proposed methods is given through case studies of different cost-inclusion scenarios, as well as battery investment-cost scenarios. An evaluation with a sample intraday market data set, collected throughout 2017 in Germany, offers a potential monthly revenue of up to 8762 EUR/MWh cap installed capacity, without accounting for the costs attributed to energy losses and battery degradation. While this is slightly above the revenue attainable in a reference application—namely, primary frequency regulation for the same sample month (7716 EUR/MWh cap installed capacity)—the situation changes if costs are considered: The optimisation reveals that losses in battery ageing and efficiency reduce the attainable profit by up to 36% for the most profitable arbitrage use case considered herein. The findings underline the significance of considering both ageing and efficiency in battery system dispatch optimisation.

Dynamics of an integrated solar photovoltaic and battery storage nanogrid for electric vehicle charging

In this paper, the performance of a renewable Solar Photovoltaic (PV) nanogrid — here defined as a small-scale power system, which comprises a single domain for control, reliability, and power quality — is assessed for Electric Vehicle (EV) charging. A nanogrid testbed, containing PV as the power supply, twenty EV charging stations, a Battery Energy Storage System (BESS), and a smart-inverter is connected to a primary feeder on the University of California, Irvine (UCI) Microgrid. We present four different smart-inverter control algorithms that govern battery dispatch for different energy management goals. The control algorithms were evaluated with respect to four figures of merit: (1) Renewable Penetration, (2) Voltage Profiles, (3) Net Power Flows, and (4) PV curtailed. A steady-state power flow model of the nanogrid was developed to study the local power quality. The control algorithms were able to successfully use the battery to shift the nanogrid peak load and also limit the nanogrid demand to a given threshold. It is shown that the nanogrid was able to offset the EV daily charging loads completely. In this same mode, the renewable contribution to EV charging was 80%, and the overall solar penetration (accounting for export and battery charge) was 89%.

A Quadratic Programming Based Optimal Power and Battery Dispatch for Grid-Connected Microgrid

In this paper, the concept of end-user-driven microgrid is introduced, and an economic dispatch framework is proposed for optimal power and battery scheduling of such a microgrid. The main advantage is that unlike other centralized approaches reported in the literature, the proposed architecture provides end-user flexibility, optimally manages the battery minimizing the power required from the grid, and at the same time ensures grid-level reliability. The proposed optimal scheduling is first validated on a grid-connected microgrid, including a photovoltaic (PV) farm and a battery, considering the grid as a dispatchable power source. Then, the architecture is compared with one of the existing centralized optimization approaches. To demonstrate the applicability and scalability, the architecture is further evaluated on a modified IEEE 33 bus distribution feeder, including multiple microgrids suitable for real-time implementation. The test results indicate that the approach is optimal, scalable, feasible, improves feeder reliability, and provides maximum utilization of the PV arrays.

Optimal Scheduling of Battery Energy Storage for Grid-Connected Load Using Photovoltaic System (PV) via Binary Particle Swarm Optimization (BPSO)

This paper presents an optimal dispatch of battery storage and its economic viability with a photovoltaic system. There are four modelled scenarios based on the combination of interruptible load program and the time-of-use scheme. The scenarios were modelled using a Binary Particle Swarm Optimization and were simulated using Matlab v6. In all the scenarios, this model successfully optimizes the battery dispatch scheduling while simultaneously minimizes the DU’s penalty from exceeding the maximum allowable power demand. This algorithm also optimizes the linearly forecasted demand for the next six year for all the scenarios. Then, an economic analysis for the possible investment to the combined BESS and PV system is conducted through the comparison of the payback periods of each scenario. The first scenario is implemented without ILP and a ToU scheme and has 79.86 payback years. With ILP scheme only, the second scenario has 33.37 payback years. Then the third scenario with ToU scheme only has a 30.29 payback years. Finally, the fourth scenario, with both ILP and ToU schemes, shows the fastest recovery of the investment with 21.57 payback years. Thus the combination of both ILP and ToU schemes provide the best economic benefit. Though the current proposed system is still not economically feasible however the foreseen positive trends on solar and battery technologies will make this system viable.

Dispatch Analysis of Off-Grid Diesel Generator-Battery Power Systems

Abstract Diesel generator (DG)–battery power systems are often adopted by telecom operators especially in semi-urban and rural areas of developing countries. System dispatch is one of the key factors to hybrid power system integration. The contradiction between battery dispatch and DG dispatch in DG–battery power systems is that shallow and medium cycling is preferred for long battery life, while deep cycling is preferred for DG fuel and maintenance saving. In this paper, two dispatch regimes, A of full cycle charge strategy and B of partial state of charge (PSOC) strategy, and the corresponding SOC (state of charge) set points of the DG–battery power system are analysed and compared in terms of system operational expenditure (OPEX) and net present cost (NPC). The system OPEX mainly consists of fuel-related, filters-related and battery bank replacement costs. The simulation programme is established based on system efficiency calculations and battery charging regimes. The results show that (1) shallow cycling may bring long DG running time and high fuel consumption, while deep cycling is in favour of reducing DG running time and fuel consumption; (2) shallow cycling is in favour of battery life under Regime A, while deep cycling is in favour of battery life under Regime B; (3) depth of discharge (DOD)$$ \in $$[0.8, 1.0] leads to the lowest NPC for both Regime A and Regime B; (4) Regime B wins with not large difference before the battery bank replacement happened, and after then Regime A wins.

Energy dispatch schedule optimization for demand charge reduction using a photovoltaic-battery storage system with solar forecasting

A battery storage dispatch strategy that optimizes demand charge reduction in real-time was developed and the discharge of battery storage devices in a grid-connected, combined photovoltaic-battery storage system (PV+system) was simulated for a summer month, July 2012, and a winter month, November 2012, in an operational environment. The problem is formulated as a linear programming (LP; or linear optimization) routine and daily minimization of peak non-coincident demand is sought to evaluate the robustness, reliability, and consistency of the battery dispatch algorithm. The LP routine leverages solar power and load forecasts to establish a load demand target (i.e., a minimum threshold to which demand can be reduced using a photovoltaic (PV) array and battery array) that is adjusted throughout the day in response to forecast error. The LP routine perfectly minimizes demand charge but forecasts errors necessitate adjustments to the perfect dispatch schedule. The PV+system consistently reduced non-coincident demand on a metered load that has an elevated diurnal (i.e., daytime) peak. The average reduction in peak demand on weekdays (days that contain the elevated load peak) was 25.6% in July and 20.5% in November. By itself, the PV array (excluding the battery array) reduced the peak demand on average 19.6% in July and 11.4% in November. PV alone cannot perfectly mitigate load spikes due to inherent variability; the inclusion of a storage device reduced the peak demand a further 6.0% in July and 9.3% in November. Circumstances affecting algorithm robustness and peak reduction reliability are discussed.