1. Introduction
The renewable energy market is growing at a rapid pace and its development is fundamental for reaching global climate targets [
1,
2,
3,
4]. Yet, advancement is not solely made by investments and expansion of renewable energy generation capacity, but also by thoroughly understanding existing energy needs in order to optimize efficiency and ultimately reduce the carbon intensity of generation and gain benefits for prosumers (small-scale consumer with microgeneration and/or storage) [
5,
6]. In order to achieve this, it is vital to understand current and future energy needs, which is a challenging task due to increasing uncertainties in supply and demand from new loads (e.g., electric vehicles [
7,
8]) and intermittent generations from renewable sources. Prosumer energy needs, together with the growing use of dynamic energy tariffs by utility providers, have underpinned the critical importance of battery storage technologies coupled with agile and accurate forecasting algorithms for efficient scheduling.
Furthermore, whether within mature energy markets or developing energy communities, the role of consumers and prosumers is of increasing importance due to several factors, across social, economic, and environmental metrics. Due to fuel poverty and the criticality of energy access, there is a need to improve accessibility to energy services. Economically, citizens are exposed to geopolitical influences on energy costs [
9], amplifying fuel poverty and underpinning economic inflation. Environmentally, 73.2% of the global carbon emission in 2016 was by the energy sector, including the use of energy in buildings, industries, and transport. Moreover, the generation of electricity and heating in residential and commercial buildings made up 17.5% of global carbon emissions [
10]. Therefore, it is necessary to accelerate the local generation and storage of renewable energy.
Considering a household or a community with either individual or shared renewable energy assets, e.g., solar panels or wind turbines and battery storage, a battery scheduling algorithm should handle the matching of demand and supply of energy efficiently, i.e., maximizing renewable energy usage and minimizing energy imports (and costs) from the central grid. Besides this battery scheduling problem, there are many potential key factors in the setup of the renewable energy assets, e.g., battery type or size and individual versus shared assets, that play a role in the overall efficiency and profit gained by a battery scheduling algorithm [
11,
12,
13].
As forecasting techniques continuously evolve and the solution space for efficiently managing energy using battery scheduling algorithms remains suboptimally covered [
14,
15], this paper studies and defines an efficient heuristic battery scheduling algorithm that can be coupled to any forecasting technique to maximize the prosumer profit. Ultimately, exploiting future knowledge should optimize the matching of supply and demand and lead towards improving the efficiency of renewable energy assets.
The particular model of the energy management system that will be considered consists of the following components:
Solar photovoltaic(s) or wind turbine(s) encapsulated as generated power;
Battery energy storage system (BESS) of a community or household;
Household or community represented as prosumer demand;
Central grid attachment via some dynamic energy plan.
Prosumer demand can be covered by power from local renewable generation, discharging the battery or importing energy from the grid. The battery can export energy to the grid and charge via locally generated power and, moreover, excess generated power can also be sold directly to the grid. Note that, in this work, we focus on using the battery to integrate local renewable generation, not price arbitrage with the grid; hence, experimenting with charging via imports from the grid is left to future work.
The outline of this paper is as follows:
Section 2 covers related work that allows the proposed smart battery scheduling algorithm to be compared and reviews the feasibility of forecasting techniques being able to deliver.
Section 3 explores and defines the heuristics of the proposed smart battery scheduling algorithm. Using the materials and methods presented in
Section 4, an assessment of the proposed smart battery scheduling algorithm by conducting various experiments will be demonstrated in
Section 5 and debated in
Section 6. Finally,
Section 7 presents the conclusions and highlights future work.
2. Related Work
To be able to compare the heuristic approach of the smart battery scheduling algorithm and assess the feasibility of the forecasting techniques being able to deliver, several related research efforts are discussed in this section.
Facing the financially driven challenge of managing energy in certain battery systems has resulted in the evolution of many different techniques to achieve performance. There are two main types of approaches to battery scheduling algorithms proposed in the prior literature: optimization-based and heuristic-based.
Linear programming (LP) has been extensively studied for the battery scheduling problem. Torres et al. [
16] consider the optimal energy scheduling of photovoltaic (PV) and conventional energy generations in addition to a battery, and linear programming is applied to optimize the operational cost. Luna et al. [
17] and Elkazaz et al. [
18] formulate the optimal day-ahead energy scheduling of a microgrid with renewable energy sources and a battery as a mixed-integer linear programming (MILP). Similarly, Nguyen et al. [
19] use MILP to minimize the day-ahead operational cost of a multi-microgrid system with a demand response program. Furthermore, Couraud et al. [
20] also study residential battery scheduling using MILP, considering operational cost as well as battery depreciation cost. Given perfect data (i.e., perfect forecasts with no uncertainty), LP approaches can be used to determine the
optimal schedule, however, they require a wide lookahead window, suffer from high computational complexity, and often assume accurate knowledge of future generation and demand. There do exist some robust optimization methods under uncertainty: for example, Zhang et al. [
21] consider PV output uncertainty in the energy scheduling of a multi-microgrid system. However, only fixed tariffs have been considered in their work. Other optimization methods include dynamic programming [
22], quadratic programming [
23,
24], genetic algorithm [
25,
26], particle swarming [
26,
27,
28,
29], honey bee mating [
30], as well as machine learning techniques, most notably reinforcement learning [
31,
32,
33].
Though the optimization and scheduling methods have been shown to reduce importing energy as well as the total energy costs, they can be computationally costly and often rely on an accurate forecast of energy generation and demand. Rule-based algorithms are another branch of battery scheduling methods that have the advantage of being highly efficient and adaptive to incoming input (generation, demand). Fitting in this direction, and representing a key starting point for this work is the model of Norbu et al. [
34], which presented a highly efficient and easily implementable heuristic-based battery scheduling algorithm. The algorithm can be depicted as a concise decision tree that determines whether to interact with the battery, i.e., charge or discharge, or interact with the central grid, i.e., sell or buy energy, based on the current residual power and battery state—see
Figure 1. The approach performs well; when not considering (forecasted) future data and using static pricing, it delivers solid results. Yet, heuristic-based methods can further benefit from incorporating predicted forecasts. Ouedraogo et al. [
35] show that their proposed rule-based battery scheduling method with predicted forecasting of 6 h ahead using ARMA achieved 94.9% relative performance to the baseline LP method.
Given that
Section 5 aims to assess the stability of the proposed smart battery scheduling algorithm not by using any actual forecasting technique but by using a range of various uncertainties based on actual data, it is critical to justify the feasibility of various uncertainties by denoting the ability of several techniques (with different complexities) used to forecast certain types of data, as explored below.
The related body of literature regarding wind predictions is as follows. O’Brien and Ralph [
36] found an error of 25–30% in true wind speeds for forecasts of 30 h by evaluating the performance of a wind-forecasting system that utilised a Numerical Weather Prediction (NWP) model and was operated in a similar environment as the data used in this paper. Forbes and Zampelli [
37] describe the accuracy of wind energy forecasts in the UK and found an energy-weighted RMSE of 32% for forecasting a day ahead. The energy trading market is leading (also in complexity) and can accurately forecast 36 h ahead at high frequencies using ensemble learning as described by Suárez-Cetrulo et al. [
38], which shows several techniques obtaining a scaled RMSE and scaled mean absolute error of less than 1 × 10
. Another promising long-term prediction model is demonstrated by Torabi et al. [
39], a cascade neural network that is able to improve one-day-ahead forecasts by 84% and one-week-ahead predictions by 73% based on the RMSE of other already progressive prediction models. Skittides and Früh [
40] propose a wind forecasting tool based on Principal Component Analysis (PCA), which is trained on past data to predict wind speeds using an ensemble of dynamically similar past events, and show good performance in forecasting the wind up to 24 h ahead.
Forecasting energy consumption based on electricity consumption data provides promising results, presumably due to the nature of repetitive and alike human behaviour causing stable patterns in the data, of 62.5 h ahead with a prediction error of around 10% using the TBATS model (which forecasts time series based on multiple seasonalities) according to Gellert et al. [
41]. The combination of weather and load prediction alone can already decrease operation costs substantially using a hybrid machine learning strategy of Faraji et al. [
42]: a multilayer perceptron (MLP) artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and radial basis function (RBF) ANN.
While tariffs are often known for various types of contracts, some agile contracts or energy markets call for predicting electricity prices as future tariffs remain unknown. The energy trading sector has led to the proper development of forecasting tools in this area, even though a decade review by Lu et al. [
43] shows the serious challenge of predicting electricity prices due to many variables: economic factors, trade factors such as cross-border energy flow, policy factors, environmental factors, calendar factors such as holidays, and lastly general consumption, production, supply, storage, and capacity play a role in energy prices. Using artificial neural networks, day-ahead predictions with a mean absolute percentage error of 7.8% have been achieved [
44].
6. Discussion
After introducing a smart battery scheduling algorithm defined in
Section 3 and putting it to the test in
Section 5 by simulating a manifold of scenarios based on
Section 4, this section will reflect on particularities present in the conducted experiments.
Firstly, a conceivably anomalous sight of more profitable simulations with respect to the simulations using perfect forecasts can be seen in the outcomes of some experiments testing the robustness of the algorithm in
Section 5.2. A probable justification for this occurrence could be that the lack of knowledge after the lookahead window causes a suboptimal decision no matter the accuracy of the forecast, as the lookahead window does not span the range of timestamps that would influence the perfect decision in retrospect. From all the detailed results of simulations for various sizes of
with perfect forecasts, presented in
Appendix A and
Appendix B, the total operational cost, i.e.,
, with respect to
follows the shape of a positive-indexed reciprocal function, i.e.,
for
, and
does not decrease, which confirms that more (accurate) knowledge guides the smart algorithm to make more profitable decisions. Based on said beliefs and findings, it seems that some of the imperfect forecasts accidentally directed the decision unknowingly towards a decision that matched the supply and demand of energy more or most optimally. Therefore, it could be worth investigating uncommon actions or trends in lookahead windows of unusually computationally large size in order to assess whether using these actions based on the estimation of some machine learning techniques in small lookahead window sizes would benefit the overall efficiency of the smart battery scheduling algorithm.
Secondly, it is essential to denote limitations regarding the experiments to critically assess the proposed smart battery scheduling algorithm appropriately. Computationally speaking, the available equipment used to run simulations had an inferior level of performance to calculate the "most optimal" profit possible where
, which would provide even finer insight into the level of performance of the simulations carried out in
Section 5.2 where randomness was introduced to sample the robustness of the smart battery scheduling algorithm. Moreover, due to a lack of available data, the simulations were run on real data yet originating from different years, thus, it merely provided a sound environment to evaluate the behaviour of the proposed smart battery scheduling algorithm, whereas true-to-life data could have supplied more relatable insights into actual cost and profit differences, especially for the energy communities that were considered in this paper. From weather to demand, all variables in the setup have a sensitive correlation, as confirmed by Hernández et al. [
55]; thus, any output cannot be truly considered genuine or useful for any recommendations besides performance testing. It would be interesting, as then the outcomes could provide practical recommendations for the size of a battery storage unit for a particular prosumer as well. In addition, various types of tariffs could be considered to gain a perspective of the usefulness of adapting to the proposed smart battery scheduling algorithm for a particular prosumer. Essentially, actual forecasting models and more real data should be explored using the proposed battery scheduling algorithm in order to advance towards practical deployment and directly improve the efficiency of renewable energy assets.
7. Conclusions and Further Work
In this paper, customised heuristics for a smart battery scheduling algorithm are developed to improve the utilisation of renewable energy and storage assets by efficient matching of energy supply and demand. For a theoretically optimal decision, the future should be taken into account, which realistically can be forecasted with the current state of technology. Based on the idea of having perfect knowledge in the complete range of time, the following broad principles should be taken into account:
A battery should never be filled more than needed as otherwise the surplus of energy could have been sold.
If battery power is considered to be utilised, while in the future excess demand needs to be covered by buying energy for a higher price than the current price, it would be beneficial to buy energy now and use battery power in the future.
If energy can be sold to the central grid on multiple occasions, it should be sold at times when the selling price is as high as possible.
If the battery is able to charge using imported energy from the grid, charging the battery with bought energy should be considered if it is more profitable than having to buy energy later at a substantially higher price.
If the available generated power and battery power cannot cover the demand, there is no other option than to buy energy. Similarly, if the battery cannot be charged more or at a higher rate, energy needs to be sold to the central grid.
Based on several thousands of simulations in areas of the UK with various probable ranges of forecasts and battery sizes, the smart battery scheduling algorithm has demonstrated to gain additional profit for both theoretically perfect forecasts and plenty more realistic forecasts by exploiting the aforementioned characteristics. On average, the results show that when a prosumer uses a battery to store generated energy, the newly proposed algorithm outperforms the baseline algorithm, obtaining up to 20–60% more profit for the prosumer from his/her energy assets with perfect forecasts. With various types and levels of simulated uncertainty in generation, demand, and tariff forecasting, the proposed algorithm has shown robustness: it produces greater profit than the baseline method and only 2–12% of profit is lost compared to perfect forecasts. The performance of the proposed algorithm increases as the uncertainty decreases, showing great promise for the algorithm as the quality of forecasting keeps improving. In conclusion, the proposed smart battery scheduling algorithm can be considered a proper improvement in the efficiency of renewable energy assets and also serve as a foundation for the heuristics of smart energy management systems using forecasting techniques.
In future work, we plan to focus on exploring how other advanced techniques from machine learning and modern AI can be leveraged to design more efficient control algorithms for integrating renewable generation, demand, and battery storage, such as, for example, those coming from Bayesian reasoning, reinforcement learning, or deep learning neural nets [
56]. The design of intelligent scheduling algorithms for this problem, that leverage recent advances in AI and the use of forecasting data, remains a key direction for further work. Other promising directions for extending the work include better integration of the battery control with renewable forecasting (for example, wind forecasting), as well as the use of our heuristics from price arbitrage and trading with the grid, taking advantage of dynamic time-of-use prices, which are increasingly used by power system operators to encourage flexible demand. Looking further ahead, it may be fruitful to integrate our approach with other smart energy problems —for example, the control of charging of electric vehicle batteries [
57], including two-directional charging in vehicle-to-grid (V2G) schemes [
58]. Additionally, algorithms such as ours could be used for distributed demand-side response [
59,
60,
61], also incorporating behavioural factors, such as perception of comfort loss by energy users [
62].