Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe economics of utility-scale portable energy storage systems in a high-renewable grid
96
0
0.0
(
0
)
Added by
Guannan He
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Battery storage is expected to play a crucial role in the low-carbon transformation of energy systems. The deployment of battery storage in the power gird, however, is currently severely limited by its low economic viability, which results from not only high capital costs but also the lack of flexible and efficient utilization schemes and business models. Making utility-scale battery storage portable through trucking unlocks its capability to provide various on-demand services. We introduce the potential applications of utility-scale portable energy storage and investigate its economics in California using a spatiotemporal decision model that determines the optimal operation and transportation schedules of portable storage. We show that mobilizing energy storage can increase its life-cycle revenues by 70% in some areas and improve renewable energy integration by relieving local transmission congestion. The life-cycle revenue of spatiotemporal arbitrage can fully compensate for the costs of portable energy storage system in several regions in California, including San Diego and the San Francisco Bay Area.
rate research
Read More
This paper proposes a nondominated sorting genetic algorithm II (NSGA-II) based approach to determine optimal or near-optimal sizing and siting of multi-purpose (e.g., voltage regulation and loss minimization), community-based, utility-scale shared energy storage in distribution systems with high penetration of solar photovoltaic energy systems. Small-scale behind-the-meter (BTM) batteries are expensive, not fully utilized, and their net value is difficult to generalize and to control for grid services. On the other hand, utility-scale shared energy storage (USSES) systems have the potential to provide primary (e.g., demand-side management, deferral of system upgrade, and demand charge reduction) as well as secondary (e.g., frequency regulation, resource adequacy, and energy arbitrage) grid services. Under the existing cost structure, storage deployed only for primary purpose cannot justify the economic benefit to owners. However, the delivery of storage for primary service utilizes only 1-50% of total battery lifetime capacity. In the proposed approach, for each candidate set of locations and sizes, the contribution of USSES systems to grid voltage deviation and power loss are evaluated and diverse Pareto-optimal front is created. USSES systems are dispersed through a new chromosome representation approach. From the list of Pareto-optimal front, distribution system planners will have the opportunity to select appropriate locations based on desired objectives. The proposed approach is demonstrated on the IEEE 123-node distribution test feeder with utility-scale PV and USSES systems.
Though considerable effort has been devoted to exploiting generation-side and demand-side operational flexibility in order to cope with uncertain renewable generations, grid-side operational flexibility has not been fully investigated. In this review, we define grid-side flexibility as the ability of a power network to deploy its flexibility resources to cope with the changes of power system state, particularly due to variation of renewable generation. Starting with a survey on the metrics of operational flexibility, we explain the definition from both physical and mathematical point of views. Then conceptual examples are presented to demonstrate the impacts of grid-side flexibility graphically, providing a geometric interpretation for a better understanding of the concepts. Afterwards the formulations and solution approaches in terms of grid-side flexibility in power system operation and planning are reviewed, based on which future research directions and challenges are outlined.
A community integrated energy system (CIES) with an electric vehicle charging station (EVCS) provides a new way for tackling growing concerns of energy efficiency and environmental pollution, it is a critical task to coordinate flexible demand response and multiple renewable uncertainties. To this end, a novel bi-level optimal dispatching model for the CIES with an EVCS in multi-stakeholder scenarios is established in this paper. In this model, an integrated demand response program is designed to promote a balance between energy supply and demand while maintaining a user comprehensive satisfaction within an acceptable range. To further tap the potential of demand response through flexibly guiding users energy consumption and electric vehicles behaviors (charging, discharging and providing spinning reserves), a dynamic pricing mechanism combining time-of-use and real-time pricing is put forward. In the solution phase, by using sequence operation theory (SOT), the original chance-constrained programming (CCP) model is converted into a readily solvable mixed-integer linear programming (MILP) formulation and finally solved by CPLEX solver. The simulation results on a practical CIES located in North China demonstrate that the presented method manages to balance the interests between CIES and EVCS via the coordination of flexible demand response and uncertain renewables.
This paper considers the optimal dispatch of energy-constrained heterogeneous storage units to maximise security of supply. A policy, requiring no knowledge of the future, is presented and shown to minimise unserved energy during supply-shortfall events, regardless of the supply and demand profiles. It is accompanied by a graphical means to rapidly determine unavoidable energy shortfalls, which can then be used to compare different device fleets. The policy is well-suited for use within the framework of system adequacy assessment; for this purpose, a discrete time optimal policy is conceived, in both analytic and algorithmic forms, such that these results can be applied to discrete time systems and simulation studies. This is exemplified via a generation adequacy study of the British system.
Energy storage is expected to play an increasingly important role in mitigating variations that come along with the growing penetration of renewable energy. In this paper, we study the optimal bidding of an energy storage unit in a semi-centralized market. The energy storage unit offers its available storage capacity and maximum charging/ discharging rate to the operator; then the operator clears the real-time market by minimizing the total cost. The energy storage unit is paid/ charged at locational marginal price (LMP). The problem casts down to a bilevel optimization problem with a mixed-integer lower-level. An improved surrogate-based method with the combined spatial-temporal entropy term is developed to solve this problem. Numerical examples demonstrate the scalability, efficiency, and accuracy of the proposed method.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
