1.1. Motivation
Decarbonisation, digitalisation and decentralisation are transforming energy systems across all continents and supporting the sustainability drive in both industry and society. Smart grids are a predominant feature in this respect and are essentially electrical grids that include a variety of interoperable communication and control devices to optimally (or near-optimally) facilitate the production and distribution of electricity. Smart grids allow for better integration and management of volatile renewable energy sources, flexible transmission resources, energy storage devices, electric vehicles, microgrids and controllable loads; they are key enablers in the decarbonisation of both industry and society [
1]. This article is concerned with the use of localised digital control and optimization techniques applied to distributed energy storage devices, within a wider smart grid context.
Battery energy storage systems (
BESS) play a significant role in improving grid stability, and when employed locally can help increase the efficiency and effectiveness of potentially volatile distributed generation units (DGs) such as solar panels and wind turbines through provision of local buffering services [
2].
BESS have gained continuous research attention due to these features and given substantial advancement in battery technologies and increasing decentralisation of grid operations, localised controls are receiving much attention [
2,
3]. This has resulted in the introduction of various battery control and management algorithms, and led to the development of batteries with an increased capacity-to-size ratio featuring faster charging and discharging rates, longer life spans and relatively lower prices [
2,
4,
5]. The main parts of the
BESS are the charge/discharge decision-making algorithms which are required to ensure optimum use of the battery and maximise the utilisation of the DGs over a given period, and the monitoring and state estimation techniques which are deployed for battery situational awareness and degradation analysis [
1]. Although there have been many developments in research in these areas, to date there has been, with some exceptions, a noticeable lack of technology transfer beyond laboratory prototypes and into real-world implementations of more advanced sensing, estimation, and charge controls for
BESS [
2].
As argued above, smart microgrid energy systems based on renewables are the key players in meeting the rising demand for electricity while maintaining greenhouse gases (GHG) emissions at acceptable levels. Due to technological advancement and declining prices solar photovoltaic (
PV) panels, in particular, have become increasingly popular in climates with suitable insolation levels. This is evidenced in the total
PV capacity installed globally, which increased from 40 to 219 GW between 2010 and 2015 and about 7% of the total global energy generation in 2020 [
6]. Power grids, on the other hand, face challenges in integrating electrical power generated by renewable energy resources (RES) such as solar panels and wind turbines. Among other complexities, the availability of RES power depends on natural resources that are uncontrollable by humans, which brings more complexity to the management of the power generation systems. Although
BESS can provide buffering solutions in these situations, to achieve maximum benefits then efficient monitoring and degradation-aware charge dispatch algorithms are required for operation in real-world environments with fluctuations in energy prices, local supply availability, and local demands.
1.2. Related Work
The control of distributed energy storage devices in smart grid applications involves the coordinated management of many smaller energy storages, typically embedded within microgrids or sub-networks in a wider, grid-of-sub-microgrids situation [
2]. There has been much recent interest related to controlling aspects of supporting power-sharing balance and sustainability, increasing system resilience and reliability, and balancing distributed state of charge; please refer to Al-Saadi et al. for a recent comprehensive review of this area [
2]. In general, control of energy storage mechanisms can be sub-divided into decentralized, centralized, multiagent, and intelligent/optimal control strategies [
2]. A potentially large range of services can also be provided by these storages, each having differing control complications and complexity of proposed solutions.
The typical standard hierarchical control architecture of a decentralised, system-of-systems (grid-of-microgrids) model network with storage can be classified into three typical levels, which relate to hierarchical and specific regulatory roles in an AC-connected, DC-bus based-microgrid [
2]. These control levels provide specific services, and are broadly explained as follows:
- (1)
Primary Control: The objective is to regulate the load sharing of distributed energy resources and storage via the local control of output voltage and frequency to attain balanced and autonomous operation of these distributed systems [
7,
8]. The prototypical strategy is droop control, which dispatches local storage based upon local active and reactive power estimates and frequency voltage/measurements and requires no time-critical communication to a centralised entity [
9].
- (2)
Secondary Control: Secondary control has the responsibility of correcting system-wide voltage and frequency offsets that are achieved by the primary control. Therefore, and an entity to play the role of a system-wide or local system state observer to provide trim commands for the primary control is required. Reactive power-sharing, accurate frequency regulation and PQ compensation are prototypical services provided, using trim commands for frequency and voltage [
10,
11]. Typically, time-critical communication to a centralised entity playing the role of system-wide observer is required [
9]. In a decentralised (or partially decentralised) solution, communications to a central entity are not required (or kept to a minimum) at the expense of added local complexity.
- (3)
Tertiary Control: This is the highest control level of the control hierarchy. Tertiary Control is typically liable for managing gross active and reactive power entering or leaving the local microgrid, to solve system-wide optimal power flow problems and constraints (OPF) [
12]. Again, an entity to play the role of a system-wide state observer is required to solve the OPF and provide local power flow commands. Again time-critical communication to a centralised entity playing the role of system-wide observer is required [
9]. In a decentralised (or partially decentralised) solution, communications to a central entity are not required (or kept to a minimum) at the expense of added local complexity.
In the context of the current work, the focus is upon partially decentralised control of BESS to support system resilience and integration of renewables and is principally concerned with tertiary controls in which the BESS is dispatched locally to meet system-wide balancing constraints. In this situation, the only communication requirement to a central entity is intermittent, soft-real time and is related to market-based price signals for importing/exporting energy. Loads and local renewable supply are forecasted locally, and the storage is dispatched based upon the solution of a local real-time economic dispatch algorithm which takes storage degradation into account.
With respect to similar previous works in the area, in [
13,
14] solar
PV and battery storage along with operation scheduling algorithms were implemented, wwhereas
BESS without RES generation was also presented by authors in [
15]. The battery is charged from the grid when the demand is low and discharged when the demand increases. By utilizing
BESS, power generation sources can be run at optimum, while the energy storage accounts for variation in the demand.
BESS should be of a suitable range of storage and power capacity to function over the required duration and can react instantaneously to the grid demand variations.
Technologies such as compressed air energy storage facilities [
16], flywheels [
17,
18], etc., have been developed for the purpose of storing large-scale energy generation. However, some of these technologies are limited in their site dependence and response capabilities. Among the different types of energy storage, batteries have relatively higher energy efficiency and offer flexible configurations for different application requirements without geographical conditions [
19]. The advancement in newer battery chemistries has enabled a wide range of battery options for storage applications. Although at present,
BESS accounts for a small portion of energy storage within the grids, they are seen as an irreplaceable option in distributed new energy integration and ancillary grid operations [
20,
21].
BESS is required to store electricity; the stored electricity can then be used when needed with the aim to reduce the electricity consumption cost and peak loads by increasing the penetration rate of RES and maximizing the self-consumption of its production. The electrical power is stored when it is available and/or price is low and used when it is unavailable and/or price is high. To achieve the optimum operation of the
BESS, an optimal schedule algorithm is required to decide when to charge, discharge or hold (no charge and no discharge) the battery. The algorithm should consider electricity consumption, RES generation and electricity prices.
With the increasing development and widespread deployment of electrical vehicles (EVs), the idea of using EV batteries as portable power storage systems have been proposed. The EVs’ batteries are used to store electricity when the EV is parked and then the stored electricity can be used to balance the grid. Storing electricity when there is a surplus and selling electricity back into the grid when there is a wider demand. A considerable amount of research has been done to investigate the applicability of this approach and a significant number of
BESS has been proposed with the aim is to maximize the benefits to grid operators and EV owners. As such,
BESS is considered the fastest growing type of energy storage technology [
22]. However, most of these studies have considered aspects and limitations that are directly related to the use of the EVs such as the habits and preferences of the EVs’ owners as well as travel and waiting time [
23,
24]. To ensure the performance and safe use of the EV batteries, they must be electrically and terminally monitored and controlled and accurate models to predict working voltage, capacity,
SoC and SoH are needed [
25]. These functionalities are commonly implemented within the
BESS. However, as discussed in [
2] and other works above, although many highly sophisticated control and monitoring mechanisms for
BESS have been proposed, in terms of wide-scale deployment, advanced techniques capable of real-time operation have progressed little beyond the laboratory due to complexities involved in reliable implementation on potentially resource-constrained field devices. There is clearly a pressing need to address this issue if progress is to be made on realising the potential of distributed and (partially or fully) decentralised
BESS in real-world situations.
1.3. Contributions
In this paper, an efficient rolling-horizon decision-making algorithm for optimal charging and discharging for a BESS is proposed, implemented using dynamic programming and studied in a real-world case study. The system is designed to operate under the variabilities of electricity pricing, consumption loads and renewable generation output, and to integrate with monitoring mechanisms and implement degradation-aware control features in a partially or fully decentralised system-of-systems-based smart grid solution. A generic decision-making strategy for battery charge/discharge operations in a discrete-time rolling framework is developed as a finite-input set of non-linear model predictive control (MPC) instances and a dynamic programming (DP) procedure is proposed for its real-time implementation. To progress beyond a laboratory prototype, the technique has been implemented, tested, and validated in the premises of a sports centre located at Rambla del Celler, in Sant Cugat, Barcelona, as a part of a pilot demonstration of the inteGRIDy EU-funded project. The aim of the proposed case-study system is to reduce the electricity bills of the sports centre and increase self-consumptions, thus, making the objective of the work carried out unique in its formulation. Our findings indicate that (i) the proposed technique is simple enough to be implemented on resource-constrained field devices and operate with required sample times for typical BESS requirements, and that (ii) the algorithm implementation achieved non-trivial reductions (≈15%) in energy costs and CO2 emissions. We conclude the proposed generic scheme is a promising candidate to support partially decentralised control of BESS for services related to system resilience and integration of renewables.