بهینه سازی برنامه مخابره انرژی و تجزیه و تحلیل هزینه - منفعت، سیستم های ذخیره سازی فتوولتائیک باتری های متصل به شبکه
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|23519||2013||11 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Renewable Energy, Volume 55, July 2013, Pages 230–240
A linear programming (LP) routine was implemented to model optimal energy storage dispatch schedules for peak net load management and demand charge minimization in a grid-connected, combined photovoltaic-battery storage system (PV+ system). The LP leverages PV power output and load forecasts to minimize peak loads subject to elementary dynamical and electrical constraints of the PV+ system. Battery charge/discharge were simulated over a range of two PV+ system parameters (battery storage capacity and peak load reduction target) to obtain energy cost for a time-of-use pricing schedule and the net present value (NPV) of the battery storage system. The financial benefits of our optimized energy dispatch schedule were compared with basic off-peak charging/on-peak discharging and real-time load response dispatch strategies that did not use any forecast information. The NPV of the battery array increased significantly when the battery was operated on the optimized schedule compared to the off-peak/on-peak and real time dispatch schedules. These trends were attributed to increased battery lifetime and reduced demand charges attained under the optimized dispatch strategy. Our results show that Lithium-ion batteries can be a financially viable energy storage solution in demand side, energy cost management applications at an installed cost of about $400–$500 per kW h (approximately 40–50% of 2011 market prices). The financial value of forecasting in energy storage dispatch optimization was calculated as a function of battery capacity ratio.
Adoption of advanced energy storage technologies as a means to integrate renewable energy resources into electric grids will dramatically increase in the next decade. 28 states in the United States of America have enacted mandatory renewable portfolio standards (RPS) and 5 additional states have adopted voluntary RPSs. RPSs require electricity providers to obtain a minimum percentage of their power from renewable energy resources by a certain date . The state of California has set an ambitious RPS of 33% renewable electricity generation by the year 2020  and passed legislation to determine energy storage procurement targets for both privately and publicly owned utilities . Although critical applications for large scale energy storage (and the associated costs, benefits and market potentials) have been clearly identified  and , dispatch strategies for stored energy that maximize the financial value of combined renewable generation and energy storage systems (hereafter RSS) are not well quantified or understood in an operational context . Many models have been developed to determine optimal scheduling for stored energy dispatch in RSSs. The objectives of these modeling studies can be broadly classified in two categories, utility side applications and demand side applications . Utility side applications focus on optimizing properties of the RSS output that are economically beneficial to electric utilities (e.g. renewable capacity firming, transmission and distribution upgrade deferral, transmission support, etc.). The financial benefits associated with some utility side applications may be difficult to quantify (e.g. transmission support). Demand side applications optimize the economics of the RSS when the system is installed “behind the meter”. In this case economic benefits are usually quantified in terms of energy bill savings for the RSS owner who purchases power from an electric utility (e.g. time-of-use energy cost management, demand charge management, etc.). Lee and Chen  used an advanced multi-pass dynamic programming (AMPDP) algorithm to optimize contract capacities and optimal energy storage capacity of stand-alone BESSs for utility customers that incur time-of-use (TOU) electricity rates. They found that optimal BESS capacity could be determined and varied significantly based on the customer's load profile. A number of studies have investigated optimal energy storage capacity and dispatch, and economics for PV+ systems.1 Su et al.  implemented a closed-loop control system to modulate power output from a PV+ system for demand charge management, TOU energy price arbitrage, emergency power supply and transmission support. Su et al. concluded that the economic viability of PV+ systems is site specific and depends strongly on the end user load shape, utility rate schedule, PV+ capacity and choice of application. However, their evaluation only considered a single PV+ system with fixed PV nameplate rating and battery capacity. Hoff et al.  studied the economic benefits of PV+ for emergency power supply and demand charge management applications for typical industrial customers. Hoff et al. found that financial benefits from emergency power supply exceeded benefits from demand charge management; however, they assumed that the entire battery capacity would be devoted to one application and only considered two PV+ systems with fixed PV nameplate rating and battery capacity. Shimada and Kurokawa  modeled annual energy bill savings for a PV+ system over a range of battery capacities. They used an approximate insolation forecast and a load forecast to determine the amount of night time charging required to minimize the cost of energy purchased by the customer from the electric utility during the following day. Shimada and Kurokawa found that the value of the PV+ system was significantly increased by using day-ahead, hourly insolation and load forecasts to inform the energy storage dispatch scheduling algorithm, and they identified optimal battery capacities in terms of end user peak load. Ru et al.  used a mixed integer linear programming (MILP) framework to determine optimal battery energy capacity (in the context of marginal energy cost) for a PV+ system, and implemented a peak reduction objective assuming perfect net load forecasts. The most comprehensive model to quantify the economic value of general RSS in demand side applications is the Distributed Energy Resources Customer Adoption Model . DER-CAM minimizes costs of operating on-site customer generation considering combinations of many different distributed generation technologies, dispatched in a variety of demand side applications, and electrical tariffs.2 Stadler et al.  used DER-CAM to study demand charge management and CO2 emissions minimization strategies in PV+ systems. Their results showed that for demand charge management it is most economically efficient to charge batteries from the electric grid during off-peak hours, while charging batteries directly from zero emissions PV generation for CO2 minimization results in extraordinarily high energy costs to the customer. In this paper we consider an idealized PV+ system in which a PV array and a Lithium-ion battery array are connected to the utility electric grid (Fig. 1). The goal is to determine the optimal dispatch schedule for the energy stored in the battery to achieve a preset amount of load peak shaving (i.e. demand charge management). The optimization algorithm is formulated as a linear program (LP) and leverages day-ahead PV power output and load forecasts with regular updates to determine the best time to charge or discharge the battery subject to basic dynamical and electrical performance constraints of the PV + system. System economics were quantified by the net present value (NPV) of the battery. The financial value of PV power output and load forecasts was calculated in an energy bill minimization application of the PV+ system. We also computed the market price at which large scale (240–1270 kW h), Lithium-ion battery energy storage becomes financially viable in demand side, energy bill minimization applications. The model formulation and structure are described in Section 2, results from analysis of model output are presented in Section 3. Sections 4 and 5 are a discussion of our results and conclusions. Full-size image (16 K) Fig. 1. A schematic of the system model illustrating the important components and power flows; the PV+ system is delineated by the dashed line. Positive and negative symbols indicate sign conventions for active power flows. Because the inverter is assumed to be lossless it is not shown in this diagram. The battery management system is included in the battery, which allows “black box” treatment of complex electrical dynamics and transients within the battery.
نتیجه گیری انگلیسی
We developed a linear programming routine to optimize the energy storage dispatch schedule for a grid-connected, combined photovoltaic-battery storage system (PV+ system). The optimization strategy targets demand charge management through a targeted peak load reduction, and leverages PV power output and load forecasts to determine the best trajectory for the battery storage output power in order to minimize demand charges. We simulated a broad range of PV+ system designs and performed a cost analysis to compare the financial benefits of our optimized energy storage dispatch schedule with basic off-peak/on-peak charging/discharging and real-time dispatch strategies. The performance and value of the optimization method were quantified in terms of energy bill savings attainable over the lifetime of the battery array. The net present value (NPV) of the battery array increased significantly (in the range $100k–$450 k – or $220/kW h to $270/kW h – for some PV+ configurations) when energy storage was dispatched on the optimized schedule instead of the simple dispatch schedules that did not use forecast information. Lithium-ion batteries are not a financially viable storage technology in demand side, energy bill management applications at current (2011) market prices. We estimated that Lithium-ion batteries become profitable at an installed cost of about $450/kW h, which is about 45% of 2011 market prices. The value of PV power output and load forecasts for the application studied in this paper is $51,000 ± $35,000. This study underscores the need to develop tools and techniques for quantitative modeling and analysis to improve estimates of the economic value of energy storage and forecasting for both utility and demand side applications. We consider our method to be a simple yet feasible approach to that end, which is useful for energy storage manufacturers, financiers and other industry professionals seeking to quantify the value of their product and forecast investment returns.