Energy Storage Assets Research Articles

A Data-Driven Justification for Dedicated Dynamic Pricing for Residences-Based Plug-in Electric Vehicles in Wind Energy-Rich Electricity Grids

Supply curtailment in wind energy-rich electricity grids occurs when electric energy supply exceeds demand. Grid-level energy storage assets with the potential for storing the excess electric energy generated from wind are yet cost-prohibitive and prone to inefficiencies. An alternative for managing this excess energy is the targeted charging of available plug-in electric vehicles (PEVs). The different power requirements, load duration, and times of usage requires PEVs to be treated differently. Consequently, using a universal pricing mechanism may not lead to the maximum benefit for the utility and the consumers, especially when trying to rely on charging PEVs with wind energy. In this study, we use a data-driven approach to investigate an existing pricing mechanism for a city (Austin, Texas) in a wind energy-rich electricity grid (ERCOT) and with high projections of PEVs. The study provides a general framework for the wind energy-rich utilities to better evaluate their profitability and the benefits of the consumers. In our case study, the results indicate the need for an alternative pricing mechanism (e.g., time-of-use) dedicated to PEVs than the existing choices for maximizing the utility of available energy from wind in the absence of grid-level energy storage.

Maximizing the degree of autarky of a 16 house neighbourhood by locally produced energy and smart control

Abstract In this work we investigate the possibilities of ”soft-islanding” (near autarkic behaviour) a microgrid with 16 houses in the Netherlands, using the DEMKit simulation toolkit. We consider a situation where energy is generated by PV-panels as well as by a CHP and energy is stored using batteries as well as a heat buffer. This research is inspired by the Aardehuizen, which is a community in the Netherlands where the inhabitants strive to use as much as possible of their sustainably produced energy locally, motivated by the ideology that their community must act sustainably and self-sufficient, rather than by financial incentives. In a first step the proper size for the equipment is determined based on energy production and consumption data of several weeks in the year. Secondly one year simulations for several scenarios are presented and the degree of autarky for each scenario is compared. We demonstrate that a (nearly) autarkic operating microgrid can be achieved, with a proper but also with a realistic sizing of the components. Combining this proper sizing of energy generation and storage assets, with an advanced control, it is possible to achieve a degree of autarky of 99.1% over a year, meaning that less than one percent of the energy has to be imported from the main grid.

Design and implementation of a test bed for building controls

Advanced controls play an essential role toward the improvement of building operational efficiency and the integration of responsive loads in buildings for grid services. Ideally, control algorithms must be sufficiently tested and validated before they are applied on real systems. This paper presents the development and current state of such an evolving test bed to support and enable experiments on advanced controls for buildings. The test bed presented in this paper consists of nine operating buildings—which possess various types of equipment and systems having different control systems and communication mechanisms (e.g., media and protocols) used in building automation systems—on the Pacific Northwest National Laboratory campus. The test bed architecture is developed in such a way that (1) it supports interactions among the buildings and heterogeneous building components and systems, including both virtual and physical devices, e.g., heating, ventilating, and air-conditioning and lighting systems; (2) it can be easily reconfigured for different control topologies and methodologies, e.g., centralized and distributed; (3) it allows selection of communication protocols, communication media, and computation resources; and (4) it is part of a larger cyber-physical test bed that includes both physical and virtual assets on distributed renewable generation, energy storage, and power system assets. Practical application: The test bed presented in this paper can be used by industry to develop and evaluate the performance of advanced control algorithms on real systems for buildings and buildings-to-grid applications. This provides practitioners an opportunity to test the applications and modify them accordingly based on their use cases and selection criteria, e.g., a controller modulating the temperature in a hospital will have different criterion as compared to an office building.

Business model analysis for the interaction between smart grid and mobile network operators

This paper explores the interaction between mobile network operators (MNOs) and smart grid operators from an economic and strategic point of view. We first present the emerging new actors in the evolving smart grid value chain. We then focus on the role of MNOs and study the different roles they could play in this value chain, not only as connectivity providers for connecting consumers to distributed energy storage and production facilities and for ensuring demand-response, but also with regards to their expertise as Internet of Things (IoT) players and their energy generation and storage assets. Based on this ecosystem analysis, we build three business models canvasses for the MNO as a Demand Response (DR) aggregator, as a Virtual Power Plant (VPP) and as an enhanced VPP where it also acts as a prosumer with generation/storage facilities. We show that the first two services fit very well with the European context, while the third is well suited for sub-Saharan African countries.

Sharing Energy Storage Between Transmission and Distribution

This paper addresses the problem of how best to coordinate, or `stack,' energy storage services in systems that lack centralized markets. Specifically, its focus is on how to coordinate transmission-level congestion relief with local, distribution-level objectives. We describe and demonstrate a unified communication and optimization framework for performing this coordination. The congestion relief problem formulation employs a weighted $\ell_{1}$-norm objective. This approach determines a set of corrective actions, i.e., energy storage injections and conventional generation adjustments, that minimize the required deviations from a planned schedule. To exercise this coordination framework, we present two case studies. The first is based on a 3-bus test system, and the second on a realistic representation of the Pacific Northwest region of the United States. The results indicate that the scheduling methodology provides congestion relief, cost savings, and improved renewable energy integration. The large-scale case study informed the design of a live demonstration carried out in partnership with the University of Washington, Doosan GridTech, Snohomish County PUD, and the Bonneville Power Administration. The goal of the demonstration was to test the feasibility of the scheduling framework in a production environment with real-world energy storage assets. The demonstration results were consistent with computational simulations.

Controller to enable the enhanced frequency response services from a multi‐electrical energy storage system

The increased adoption of renewable energy generation is reducing the inertial response of the Great Britain (GB) power system, which translates into larger frequency variations in both transient and pseudo-steady-state operation. To help mitigate this, National Grid, the transmission system operator in GB, has designed a control scheme called enhanced frequency response (EFR) specifically aimed at energy storage systems (ESSs). This study proposes a control system that enables the provision of EFR services from a multi-electrical ESS and at the same time allows the management of the state of charge (SOC) of each ESS. The proposed control system uses a Fuzzy Logic Controller to maintain the SOC as near as possible to the desired SOC of each ESS while providing EFR. The performance of the proposed controller is validated in transient and steady-state domains. Simulation results highlight the benefits of managing the SOC of the energy storage assets with the proposed controller. These benefits include a reduced rate of change of frequency and frequency nadir following a loss of generation as well as an increase in the service performance measure which renders into increased economic benefits for the service provider.

Merchant Energy Trading in a Network

We formulate the merchant trading of energy in a network of storage and transport assets as a Markov decision process with uncertain energy prices, generalizing known models. Because of the intractability of our model, we develop heuristics and both lower and dual (upper) bounds on the optimal policy value estimated within Monte Carlo simulation. We achieve tractability using linear optimization, extending near optimal approximate dynamic programming techniques for the case of a single storage asset, versions of two of which are commercially available. We propose (i) a generalization of a deterministic reoptimization heuristic, (ii) an iterative version of the least squares Monte Carlo approach, and (iii) a perfect information dual bound. We apply our methods to a set of realistic natural gas instances. The combination of our reoptimization heuristic and dual bound emerges as a practical approach to nearly optimally solve our model. Our iterative least squares Monte Carlo heuristic is also close to optimal. Compared to our other heuristic, it exhibits slightly larger optimality gaps and requires some tuning, but is faster to execute in some cases. Our methods could enhance single energy storage asset software and have potential relevance beyond our specific application. The e-companion is available at https://doi.org/10.1287/opre.2018.1732 .

Optimal Operation of Energy Storage Systems Considering Forecasts and Battery Degradation

Energy storage systems have the potential to deliver value in multiple ways, and these must be traded off against one another. An operational strategy that aims to maximize the returned value of such a system can often be significantly improved with the use of forecasting—of demand, generation, and pricing—but consideration of battery degradation is important too. This paper proposes a stochastic dynamic programming approach to optimally operate an energy storage system across a receding horizon. The method operates an energy storage asset to deliver maximal lifetime value, by using available forecasts and by applying a multi-factor battery degradation model that takes into account operational impacts on system degradation. Applying the method to a dataset of a residential Australian customer base demonstrates that an optimally operated system returns a lifetime value which is 160% more, on average, than that of the same system operated using a set-point-based method applied in many settings today.

Energy-Centric Flexibility Management in Power Systems

Currently, power system planning practices are undergoing various transformations in an attempt to integrate efficiently significant amounts of low-carbon power generation technologies. At the heart of this efficient integration lies the need to plan for and exploit the available flexibility in power systems. In the past, the emphasis was on planning the capacity (i.e., power) of operating reserve requirements, in the form of categorized reserve types. Such approach was suitable for traditional power systems exhibiting low variability and uncertainty. The concept of power system flexibility is emerging as a way to emphasize the need to also consider the ramping capability of operating reserve needed to accommodate high variability and uncertainty arising from renewable energy integration. Prior considerations of power system flexibility, however, are found to be inadequate to handle energy-limited power system resources like energy storage assets and demand response. Hence, this paper sets to consider systematically energy limitations of operating reserve by proposing energy-based operating reserve definitions. We demonstrate the benefits of the new reserve definitions, using a receding-horizon economic dispatch integrating energy storage.

Optimal energy management system based on stochastic approach for a home Microgrid with integrated responsive load demand and energy storage

In recent years, increasing interest in developing small-scale fully integrated energy resources in distributed power networks and their production has led to the emergence of smart Microgrids (MG), in particular for distributed renewable energy resources integrated with wind turbine, photovoltaic and energy storage assets. In this paper, a sustainable day-ahead scheduling of the grid-connected home-type Microgrids (H-MG) with the integration of non-dispatchable/dispatchable distributed energy resources and responsive load demand is co-investigated, in particular to study the simultaneously existed uncontrollable and controllable production resources despite the existence of responsive and non-responding loads. An efficient energy management system (EMS) optimization algorithm based on mixed-integer linear programming (MILP) (termed as EMS-MILP) using the GAMS implementation for producing power optimization with minimum hourly power system operational cost and sustainable electricity generation of within a H-MG. The day-ahead scheduling feature of electric power and energy systems shared with renewable resources as a MILP problem characteristic for solving the hourly economic dispatch-constraint unit commitment is also modelled to demonstrate the ability of an EMS-MILP algorithm for a H-MG under realistic technical constraints connected to the upstream grid. Numerical simulations highlight the effectiveness of the proposed algorithmic optimization capabilities for sustainable operations of smart H-MGs connected to a variety of global loads and resources to postulate best power economization. Results demonstrate the effectiveness of the proposed algorithm and show a reduction in the generated power cost by almost 21% in comparison with conventional EMS.

Classification of Commercial Building Electrical Demand Profiles for Energy Storage Applications

Commercial buildings have a significant impact on energy and the environment, being responsible for more than 18% of the annual primary energy consumption in the United States. Analyzing their electrical demand profiles is necessary for the assessment of supply-demand interactions and potential; of particular importance are supply- or demand-side energy storage assets and the value they bring to various stakeholders in the smart grid context. This research developed and applied unsupervised classification of commercial buildings according to their electrical demand profile. A Department of Energy (DOE) database was employed, containing electrical demand profiles representing the United States commercial building stock as detailed in the 2003 Commercial Buildings Consumption Survey (CBECS) and as modeled in the EnergyPlus building energy simulation tool. The essence of the approach was: (1) discrete wavelet transformation of the electrical demand profiles, (2) energy and entropy feature extraction (absolute and relative) from the wavelet levels at definitive time frames, and (3) Bayesian probabilistic hierarchical clustering of the features to classify the buildings in terms of similar patterns of electrical demand. The process yielded a categorized and more manageable set of representative electrical demand profiles, inference of the characteristics influencing supply-demand interactions, and a test bed for quantifying the impact of applying energy storage technologies.

The introduction of electric vehicles in the private fleet: Potential impact on the electric supply system and on the environment. A case study for the Province of Milan, Italy

The study analyses the possible impact of the electric vehicles’ recharging activities on the electric supply system for the Province of Milan and on the global environment with a 2030 time horizon. In particular, the impact on the electric grid is seen both in terms of total electric energy consumption and in power requested to the grid. Because of the long recharging time required by the car batteries, the probability to have thousands of cars contemporary plugged-in at a given time is not negligible. On the other hand, the impact on the environment is seen in terms of CO 2 emissions reduction. Even if, at the moment, the Italian electric energy mix is mainly generated by means of thermal power stations making use of not renewable fossil fuels, the efficiency of these plants is much higher than the efficiency of a vehicle’s engine. Results obtained clearly show that electric vehicles can contribute to the overall CO 2 abatement strategy in the transport sector but at the same time without an appropriate regulation (e.g. the intelligent integration of electric vehicles into the existing power grid as decentralised and flexible energy storage asset), electric vehicles could heavily impact on the daily requested electric power.