Elsevier

Applied Energy

Volume 286, 15 March 2021, 116550
Applied Energy

An economic assessment of behind-the-meter photovoltaics paired with batteries on the Hawaiian Islands

https://doi.org/10.1016/j.apenergy.2021.116550Get rights and content

Highlights

  • Innovative methods are proposed for evaluating photovoltaics paired with batteries.

  • Key techno-economic characteristics of photovoltaics and batteries are captured.

  • Photovoltaics compensation and demand response are considered simultaneously.

  • Techniques are proposed to linearize the optimal dispatch and sizing problems.

  • Comprehensive assessment is performed by customer type across the Hawaiian Islands.

Abstract

Due to natural variability and uncertainty, the ever-increasing penetration of solar generation in Hawaii presents challenges to power grid operators to maintain reliable system operation. Demand response (DR) has the potential to be a cost-effective tool for Hawaii to reach its aggressive renewable energy goals while maintaining the reliability of power grids. The Hawaii Public Utilities Commission has approved the Hawaiian Electric Company’s revised portfolio of DR programs. The companies have released a grid services purchase agreement and subscribed an initial tranche of load into their DR programs. This paper presents innovative analytical methods and comprehensive economic assessment for distributed photovoltaics (PV) paired with battery energy storage systems (BESSs) for two new DR programs, including fast frequency response and capacity grid service. Optimal dispatch and sizing methods are proposed for the paired system considering different tariff schedules and PV compensation programs across five islands. It was found that while the best resource configuration and potential economic benefits vary with tariff structure, a BESS paired with PV can be optimally dispatched to generate multiple value streams simultaneously. Compensation from DR programs is an important value stream to help increase the cost-effectiveness of the integrated system.

Introduction

Because of the high electricity costs and rich solar resources, solar energy, especially distributed photovoltaics (PV), has developed rapidly in Hawaii during the past decade. Thanks to the net energy metering program that pays rooftop solar owners retail rates for power sent back to the grid, Hawaii became the first state in the United States to reach grid parity for PV. Today, distributed solar panels on individual homes and businesses account for a large portion of Hawaii’s renewable generation. In 2018, distributed PV in Hawaii produced 998 GWh, accounting for 39.6% of all renewable energy produced in the state [1]. Due to natural variability and uncertainty, the growing penetration of solar generation presents challenges to reliably operating the Hawaiian grids, which are each separated and relatively small. Since the existing systems can only accommodate a limited amount of renewable generation, the state has to deal with the unique situation by developing new strategies for balancing generation and load, and eventually achieve its aggressive clean energy goals—with 40% renewable energy by 2030 and 100% by 2045 [2].

Demand response (DR) has the potential to be a cost-effective tool for Hawaii to reach its renewable energy goals while maintaining power grid reliability. In 2017, the Hawaii Public Utilities Commission (PUC) approved the Hawaiian Electric Company’s (HECO’s) revised portfolio of DR programs, and in subsequent orders, approved HECO’s approach and a contract (i.e., Grid Services Purchase Agreement) with a third-party aggregator for the first phase in obtaining these resources. HECO is in the process of negotiating a second agreement, and it is expected that HECO will issue additional requests for proposals to deliver additional grid services through DR up to the approved capacity that was determined as cost-effective and has been authorized by the PUC [3]. The integrated DR programs envision various types of resources, including behind-the-meter (BTM) battery energy storage systems (BESSs) and plug-in electric vehicles, and can potentially shift hundreds of megawatts of electricity from thousands of homes and businesses. Participating owners will receive financial incentives to allow utilities to charge/discharge their batteries or change net-load patterns based on needs of the grid. With the proliferation of customer PV and BESS expected, DR programs will play an essential role in achieving sustainable and reliable grids through aggregating and using these BTM resources to facilitate the integration of renewable energy in Hawaii.

The costs of BESSs have fallen significantly over the past decade and are projected to fall further [4]. BESSs are becoming promising candidates for increasing flexibility and improving the reliability of power systems. They can be used to provide a number of grid and end-user services [5], [6] and help to improve system resilience [7]. During the past few years, many studies have been dedicated to optimal dispatch and economic evaluation of BESSs for grid services. Just to name a few, the authors of [8] present an economic assessment of utility-owned BESSs based on an optimal dispatch that captures stacked value streams from multiple grid services. In [9], a bi-level strategic scheduling model and its equivalent stochastic programming formulation are presented to maximize a load serving entity’s profit in the energy market through optimal BESS scheduling. A new mean–variance optimization-based energy storage scheduling method is proposed in [10] to address uncertainties associated with energy price forecast in power markets. In [11], the authors use realistic battery cycle degradation to re-evaluate BESS profitability for ancillary services and attempt to increase profits by mitigating this degradation. In [12], nonlinear operating characteristics of batteries are incorporated into an optimization problem to determine the optimal market participation of BESSs. In [13], the authors present a reinforcement learning solution augmented with Monte-Carlo tree search and domain knowledge expressed as dispatching rules for a BESS.

There are also studies dedicated to evaluation and/or sizing of BTM BESSs. For example, linear programming formulations are proposed in [14] for economic analysis and optimal sizing of a BESS to reduce the energy charge and demand charge. In [15], the authors present an objective quantitative analysis to identify key factors that affect the net benefits of a BTM BESS and develop simple guidelines for optimal sizing. An economic analysis is performed in [16] for a customer-installed BESS that is used to reduce their peak demand and participate in a DR program in South Korea. In [17], the authors propose an intertemporal assessment framework that maximizes the life-cycle benefit of BESS, considering both functionality and profitability degradation. In these studies, distributed renewable generation is not explicitly modeled but can be treated as a negative load. There are also studies focusing on BESSs paired with distributed PV or wind generation. The authors in [18] jointly optimize the size and operating strategy of a hybrid energy storage system and determine the Pareto-frontier of the sizes of the underlying storage elements. In [19], the authors propose a statistical approach to determine energy storage capacity based on capacity distributions in an autonomous PV/wind power generation system. The authors in [20] propose a method to simultaneously optimize the battery size and rule-based operation strategy using a multi-objective genetic algorithm for grid-connected photovoltaic-battery systems considering electricity reduction, electricity export, and peak shaving. In [21], the authors assess the impacts of different forecasting methods on BESS-PV systems with real-time or predictive control strategies. The authors in [22] develop a mixed-integer linear robust optimization to model aggregated residential BESS-PV systems for the participation in day-ahead energy markets. In [23], the authors extend pinch analysis and design space approach to optimally size battery storage for PV-based microgrids. A genetic algorithm is presented in [24] for optimal sizing of a multi-source PV/wind with hybrid energy storage. In [25], optimal sizing of PV/wind/diesel hybrid microgrids with battery storage is conducted using a self-adaptive differential evolution algorithm. In [26], the authors propose an optimal sizing method based on a genetic algorithm involving a time series simulation of a PV and battery system. A modeling and simulation framework is proposed in [27] for evaluating and sizing PV/battery systems using a genetic algorithm considering three rural application scenarios. The authors of [28] evaluate different use cases for residential BESS and PV by simulating battery operations using rate-based cycling algorithms and analyzing the prognosis of multiple battery lifetime models. Many of these existing methods are based on evolution algorithms, especially genetic algorithms, for optimal sizing. Others are analytical methods based on quantitative analysis or mathematical programming methods relying on optimization solvers. The models, formulations, rules, procedures, and algorithms used for optimal dispatching and sizing in all these methods largely depend on use cases and applications. To the best of our knowledge, there is no existing study dedicated to evaluation and sizing of BTM PV paired with batteries considering electricity bill reduction, PV compensation programs, and DR services at the same time.

HECO would benefit from a better understanding of the economic benefits to a customer participating in DR programs, and the incentive levels required to ensure customer participation. To bridge the gap, this paper presents linear programming (LP) and mixed-integer linear programming (MILP) methods and comprehensive economic analysis for optimal dispatch and sizing of BTM PV paired with batteries considering PV compensation and three DR programs as well as electricity cost reduction across the Hawaiian Islands. The main contributions of this paper are summarized as follows.

  • Models are proposed to capture the requirements and rules of different PV compensation and DR programs, express demand and energy charges as functions of power output from the paired system, and represent technical and economic characteristics of PV and BESS.

  • The optimal dispatch and sizing problems are inherently nonlinear and challenging to solve. Innovative methods are proposed to generate equivalent LP or MILP problems, which can be directly solved using off-the-shelf solvers.

  • Representative case studies are developed and carried out to assess how DR programs affect the benefits of the integrated system from the perspective of end-users and provide insights on the cost-effectiveness of pairing a BESS with PV for different customers on different islands.

The rest of this paper is organized as follows. Section 2 describes utility tariff, PV compensation, and DR programs considered in this paper. The modeling methods are given in Section 3. Section 4 presents the proposed optimal dispatch and sizing methods, as well as optimization tricks to obtain equivalent LP or MILP problems. In Section 5, comprehensive analysis and thoughtful insights are provided. Finally, Section 6 offers concluding remarks.

Section snippets

Utility tariff and incentive programs

HECO delivers electricity to customers located on the islands of Oahu, Maui, Lanai, Molokai, and Hawaii. A ratepayer’s costs depend on tariff structure and rate, as well as energy usage patterns. The tariff schedules depend on customer types and tariff rates varying by island. BESSs in combination with PV can effectively modify the energy usage pattern and thereby reduce the electricity bill. Bill reductions or credits received also depend on the selection of PV compensation programs. DR

Modeling method

To quantify the potential benefits from BTM storage in combination with PV, the technical characteristics of BESS and PV need to be reasonably represented. In addition, models need be developed to capture how the integrated system can be used to reduce electricity bills and receive compensation from PV and DR programs. This section presents the modeling method for the integrated system and its usage for different applications. Note that the BESS and PV models used in this paper have been

Optimal dispatch and sizing methods

Optimal dispatch and sizing methods are required to maximize the potential benefits from an integrated PV and BESS for electricity bill reduction and DR revenue, considering different tariff structures and PV compensation programs. In particular, the optimal dispatch problem seeks the best charging/discharging strategy to maximize the benefits for a given size of system and estimates the net benefits through post-processing. The optimal sizing problem aims to determine the best resource

Case study results

The proposed methods in the previous section are used for economic assessment by customer type across the five islands, considering different scenarios of resource configurations and availability of DR programs. The capital and O&M cost parameters for BESS and PV are adopted from [4], [7], [29], as listed in Table 2. The BESS charging and discharging efficiencies (including both battery and inverter) are assumed to be 93% and 90%, respectively. The tariff schedules are listed in Table 3 by

Conclusions

This paper sets forth a theoretical framework for assessing BTM PV paired with BESS on the Hawaiian Islands, considering the potential benefits from providing DR services. Modeling methods were presented to represent technical and economic characteristics of PV and BESS systems, and to capture how the integrated system can be used to reduce electricity cost and receive compensation from PV and DR programs. Optimization techniques and LP formulations were then proposed to determine the optimal

CRediT authorship contribution statement

Di Wu: Conceptualization, Methodology, Software, Validation, Formal analysis, Writing - original draft. Xu Ma: Methodology, Software, Formal analysis Writing - original draft. Patrick Balducci: Conceptualization, Methodology, Writing - review & editing. Dhruv Bhatnagar: Conceptualization, Validation, Writing - review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

We are grateful to Imre Gyuk, who is the Director of Energy Storage Research in the Office of Electricity at the U.S. Department of Energy , for providing financial support and leadership on this and other related work at Pacific Northwest National Laboratory.

References (29)

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This material is based upon work supported by the U.S. Department of Energy (DOE), Office of Electricity through the Energy Storage program. Pacific Northwest National Laboratory is operated for the DOE by Battelle Memorial Institute under Contract DE-AC05-76RL01830.

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