GridPeaks: Employing Distributed Energy Storage for Grid Peak Reduction Yasra Chandio∗, Aditya Mishray, Anand Seetharam∗ ∗Department of Computer Science, SUNY Binghamton, USA yDepartment of Computer Science and Software Engineering, Seattle University, USA fychandio, [email protected],[email protected] Abstract—Since peak demand dictates the costs and carbon cannot serve peak demands. Utilities need to run extra “peak- emissions in electricity generation, electric utilities are transition- ing” generators to serve the peaks. These “peaking” generators ing to renewable energy to cut peaks and curtail carbon footprint. frequently operate on fossil fuels [7] hence have greater carbon Although clean and sustainable energy source, intermittent na- ture of most renewables (e.g., solar, wind) makes it challenging emissions compared to base load generators like hydroelectric to integrate them with the traditional electric grid. Energy plants, nuclear plants, solar power [2]. Additionally, due to storage could facilitate the integration. Grid-scale energy storage higher costs of building fuel efficient generators, peaking projects have been coming up across the world, but require generators are less efficient than base load generators [7]. The huge upfront capital costs, and significant time and efforts. inefficiency adds to carbon footprint. An economic and scalable alternative to expensive centralized energy storage is to leverage distributed energy storage across To curtail electricity’s carbon footprint and cut costs, states several homes in the grid. Prior research has proposed employing are transitioning to renewable energy sources. For instance, home energy storage for cutting peak demands and electricity Hawaii plans to get 100% of its energy from renewable sources bills for customers. However, homes working individually cannot by 2045. Although a sustainable energy source, most renew- shave aggregate peaks efficaciously since not all home peaks ables are intermittent and unpredictable. The intermittence are aligned with the aggregate peaks and homes don’t have the aggregate consumption knowledge. To address these limitations, makes them incompatible with the traditional electric grid we present GridPeaks, a distributed energy storage system that that operates on the paradigm of “supply follows demand.” centrally controls the batteries of the participating homes from One possible way of bridging this gap is energy storage. a master node deployed at the grid. The master makes battery Intermittent energy can be stored in batteries when available. charging-discharging decisions based on aggregate grid demand. The stored energy can be drawn to serve demand later. We evaluate GridPeaks on real power consumption data. Our results show that GridPeaks can cut grid-wide peaks by 23% Grid-scale energy storage projects have been coming up and reduce the generation costs by up to 14%. across the world. For instance, one of the largest energy storage project, with a gigawatt power rating, is planned for I. INTRODUCTION Utah [8]. Such centralized storage installations do facilitate Eighteen of the 19 warmest years have all occurred since renewable energy integration in the grid and allow peak shav- 2001 [4]; 2016 is the warmest year on record. The temperature ing, but require huge upfront costs, planning, and significant increase is chiefly driven by increased emissions of green- engineering time and efforts. house gases (such as CO2) into the atmosphere. Electricity An economic and scalable alternative to expensive central- generation is a significant contributor to the emissions. Around ized energy storage is to leverage distributed energy storage 33% of the total U.S. energy-related CO2 emissions, in 2018, across several buildings and homes in the grid. Many homes were from electric power sector [5]. Carbon emissions from already have battery storage, for instance, homes with electric electricity generation are determined not only by the total vehicles (EVs), and photovoltaic deployments; in remote areas energy demand, but also by the magnitude of the maximum and developing nations, where the grid is unstable, battery is energy that needs to be generated per unit time, i.e., the peak used as backup during outages. As the energy storage price demand (measured in kW or MW). continues to drop and more off-the-shelf home storage solu- Peak demands dictate both the costs and carbon emissions tions become available, the storage penetration in residential in electricity generation. Grid’s installed generation and trans- sector is expected to rise further [6]. mission capacity needs to be provisioned for the tallest annual Prior research has proposed employing home energy stor- peak. Unfortunately, due to peaky nature of demand, lot of age for cutting peak demands and electricity bills for cus- the installed capacity sits idle for most time. In the U.S., only tomers [10], [11], [13], [14], [20]–[22], [25], [26]. However, about 50% of the capacity is used 100% of the time. Only 5% this approach has limitations. Using batteries to exploit time- of the time greater than 90% generation capacity is used [1]. of-use pricing could create a taller rebound peak during low The base load generators that are operational continually often price periods [20], [22]. Employing storage to cut individual home peaks could reduce aggregate peaks (as shown in [22]), but it does not achieve best results since not all home peaks 978-1-5386-7466-6/18 $31.00 © 2019 IEEE are aligned with the grid-wide aggregate peaks. Furthermore, 0.18 GridPeaks evaluation. The function is derived by scaling the 0.16 0.14 generation cost data in the figure to match the aggregate peak 0.12 demand in our micro-grid traces. 0.10 0.08 0.06 B. PeakCharge 0.04 0.02 GridPeaks leverages centrally controlled distributed battery Marginal Cost ($/kWh) 0.00 0 25 50 75 100 125 150 175 200 225 250 275 300 325 350 375 400 system to shave peaks and cut generation costs. For perfor- Generation Capacity (kW) mance evaluation, we compare GridPeaks with its distributed Fig. 1. The marginal cost to generate electricity increases as the demand alternative—PeakCharge [22]. PeakCharge proposes a large increase as homes do not have grid-level consumption knowledge, they scale distributed energy storage system in the grid that is indi- cannot shave aggregate peaks efficaciously. vidually managed by homes. In PeakCharge, every home uses Therefore, to cut peaks and generation costs effectively, an online battery charging-discharging algorithm to cut user overcome limitations of isolated battery deployments, and electricity bills in presence of peak based pricing plans. The reduce user electricity costs we present GridPeaks. Grid- idea being, if individual home peaks are shaved, the aggregate Peaks is a distributed energy storage system where batteries grid-peak would be shaved consequently. To minimize peaks, at participating homes are centrally controlled by a master PeakCharge tries to keep the home’s consumption close to node deployed at the electric grid. Since the master has its target daily average consumption. If the home’s demand is knowledge of real-time aggregate demand, it uses an online below the target average, PeakCharge charges the battery from charge-discharge algorithm to control distributed storage so the grid at a rate so as to bring up the net consumption from as to optimize the grid demand profile for peak shaving and the grid to the target average. Similarly, if the consumption is generation cost savings. above the target, PeakCharge draws a fraction of the demand Although distributed energy storage, operating indepen- from the battery to bring down the net draw to the target dently, across several buildings in the grid can cut peaks average. More details about PeakCharge are in [22]. and save electricity costs for the customers, it produces sub- C. Variable and peak based electricity pricing optimal results. Our hypothesis is that letting the grid control charging-discharging of the distributed storage at buildings can To cut peaks and generation costs, several utilities are transi- significantly boost aggregate peak reduction, and generation tioning to market-based variable electricity pricing plans such cost savings. In evaluating our hypothesis, we make the as time-of-use (ToU) pricing. The variable pricing incentivizes following contributions: 1) We detail GridPeaks’ architecture customers to lower peak demand by having a higher electricity deployed at the participating homes. 2) We present a frame- price during high demand periods (such as dinner times) and work that allows the grid to control charging-discharging of the a lower price during low demand periods (e.g., late nights). distributed energy storage from a centralized location. 3) Novel Such pricing plans are in place at several locations including revenue sharing model. If traditional peak based pricing plans Ontario [24], Illinois [15]. Additionally, to curtail peaks from are used at homes, centralized control of storage could increase commercial customers, utilities impose a peak penalty on the bill. Therefore, to incentivize participation in GridPeaks, their highest consumption peak, e.g., [3]. Hence, commercial we propose a novel revenue sharing system that employs peak customers pay for both energy consumption ($/kWh) and every based variable electricity pricing at homes, but allows the grid kW of their tallest monthly peak ($/kW). to share its generation cost savings (from GridPeaks) with D. GridPeaks’ novel electricity pricing the participating homes in proportion to their storage capacity contribution. 4) We evaluate GridPeaks in simulation using For a fair comparison with PeakCharge, we evaluate Grid- power consumption data from real homes and existing variable Peaks using a hybrid pricing plan needed by PeakCharge, electricity pricing plans. as done in [22]. The hybrid pricing bills customers for both the energy consumption ($/kWh) and the tallest peak ($/kW). II. BACKGROUND For energy consumption, we use the ToU prices from Ontario Energy Board [24]. We adopt a peak surcharge of $0.60/kW A. Electricity generation costs based on [22]. Figure 1 is derived from the actual U.S.