In this paper, we present a novel method for scheduling smart appliances and batteries, in order to reduce both the electricity bill and the CO₂ emissions. Mathematically, the scheduling problem is posed as a multi-objective Mixed Integer Linear Programming (MILP), which can be solved by using standard algorithms. A case study is performed to assess the performance of the proposed scheduling framework. Numerical results show that the new formulation can decrease both the CO₂ emissions and the electricity bill. Furthermore, a survey of studies that deal with scheduling of smart appliances is provided. These papers use methods based on MILP, Dynamic Programming (DP), and Minimum Cut Algorithm (MCA) for solving the scheduling problem. We discuss their performance in terms of computation time and optimality versus time discretization and number of appliances.

In this paper, we propose a robust approach for scheduling of smart appliances and electrical energy storages (EESs) in active apartments with the aim of reducing both the electricity bill and the CO₂ emissions. The proposed robust formulation takes the user behavior uncertainty into account so that the optimal appliances schedule is less sensitive to unpredictable changes in user preferences. The user behavior uncertainty is modeled as uncertainty in the cost function coefficients. In order to reduce the level of conservativeness of the robust solution, we introduce a parameter allowing to achieve a trade-off between the price of robustness and the protection against uncertainty. Mathematically, the robust scheduling problem is posed as a multi-objective Mixed Integer Linear Programming (MILP), which is solved by using standard algorithms. The numerical results show effectiveness of the proposed approach to increase both the electricity bill and CO₂ emissions savings, in the presence of user behavior uncertainties. Mathematical insights into the robust formulation are illustrated and the sensitivity of the optimum cost in the presence of uncertainties is investigated. Although home appliances and EESs are considered in this work, we point out that the proposed scheduling framework is generally applicable to many use cases, e.g., charging and discharging of electrical vehicles in an effective way. In addition, it is applicable to various scenarios considering different uncertainty sources, different storage technologies and generic programmable electrical loads, as well as different optimization criteria.

A novel distributed algorithm is proposed in this paper for a network of consumers coupled by energy resource sharing constraints, which aims at minimizing the aggregated electricity costs. Each consumers is equipped with an energy management system that schedules the shiftable loads accounting for user preferences, while an aggregator entity coordinates the consumers demand and manages the interaction with the grid and the shared energy storage system (ESS) via a distributed strategy. The proposed distributed coordination algorithm requires the computation of Mixed Integer Linear Programs (MILPs) at each iteration. The proposed approach guarantees constraints satisfaction, cooperation among consumers, and fairness in the use of the shared resources among consumers. The strategy requires limited message exchange between each consumer and the aggregator, and no messaging among the consumers, which protects consumers privacy. Performance of the proposed distributed algorithm in comparison with a centralized one is illustrated using numerical experiments.

A smart home is considered as an automated residential house that is provided with distributed energy resources and a home energy management system (HEMS). The distributed energy resources comprise PV solar panels and battery storage unit, in the smart homes in this study. In the literature, HEMSs apply optimization algorithms to efficiently plan and control the PV-storage, for the day ahead, to minimize daily electricity cost. This is a sequential stochastic decision making problem, which is computationally intensive. Thus, it is required to develop a computationally efficient approach. Here, we apply a recurrent neural network (RNN) to deal with the sequential decision-making problem. The RNN is trained offline, on the historical data of end-users’ demand, PV generation, time of use tariff and optimal state of charge of the battery storage. Here, optimal state of charge trace is generated by solving a mixed integer linear program, generated from the historical demand and PV traces and tariffs, with the aim of minimizing daily electricity cost. The trained RNN is called policy function approximation (PFA), and its output is filtered by a control policy, to derive efficient and feasible day-ahead state of charge. Furthermore, knowing that there are always new end-users installing PV-storage systems, that don’t have historical data of their own, we propose a computationally efficient and close-to-optimal plug-and-play planning and control algorithm for their HEMSs. Performance of the proposed algorithm is then evaluated in comparison with the optimal strategies, through numerical studies.

Nowadays, end-users can participate in demand response (DR) programs, and even slight load reductions from many houses can add up to major load shifts in the power system. Aggregators, which act as mediators between end-users and distribution system operator (DSO), play an important role here. The aggregator contracts the end-users for DR programs, plans ahead for times when customers should shift their load, and controls the load shifts in the running time. In this paper, our main focus is on planning the end-users for load shifting. Here, we first define and formulate the flexibilities (e.g., Stamina, repetition, and capacity) related to the dynamic loads such as space heating systems (SHSs) in detached houses. Assuming some end-users being contracted for DR program, based on estimation of their house characteristics and load flexibilities, an algorithm is then proposed to plan the SHSs for load shifting. In this algorithm the states in which a flexible load can be planned, kept in backup, or unavailable are considered by the aggregator. Another algorithm has been proposed here to deal with the different sources of uncertainties (which cause some of the planned SHSs to become unavailable). Numerical results are presented at the end, which discuss performance of the proposed strategy in terms of load flexibilities, load shifts in response to DR signals, and sensitivity analysis. Here, how to estimate the houses characteristics is a difficult issue, and we approximate them based on available models in the literature.

There should be a constant balance between the demand and supply of electrical power. In Nordic countries, electricity markets have been formulated in such a way so as to facilitate this balance. These markets enable purchases, through bids, for buying and selling the energy (e.g., the day-ahead market) and the reserves (e.g., the frequency containment reserve for normal operation (FCR-N)). Demand response (DR) has received increased attention in recent years since it can efficiently support bidding in these markets. Aggregators, which act as mediators between end-users and the system operator, play an important role here. The aggregator contracts a large number of end-users for DR programs, and plans and controls their heterogeneous thermostatically controlled loads (TCLs), and offers their load flexibility to the markets. Taking into account the small market value of each contributing unit, the cost for the communication and control system enabling the DR service must be kept at a minimum. In this paper, we propose a framework which is adaptable to pre-existing and newly emerging TCLs, with no need for major re-design of the local control loops. We then design a strategy for the aggregator, to predict, schedule and control the aggregated flexibility of the contracted heterogeneous TCLs, in response to the DR signals and in the presence of end-users’ behavior uncertainties. In this strategy, we have applied a recurrent neural network (RNN) which learns the aggregated consumption of end-users and predict their aggregated load flexibility. The scheduling and control algorithms are then designed with the aim of participation in FCR-N market. We show that uncertainties in the prediction and scheduling are compensated in the control stage by activating back-up resources. A numerical study on 2000 number of detached houses has been conducted, which shows available 500 kW capacity for participation in the FCR-N market.

Most existing industrial control systems (ICSs), such as building energy management systems (EMSs), were installed when potential security threats were only physical. With advances in connectivity, ICSs are now, typically, connected to communications networks and, as a result, can be accessed remotely. This extends the attack surface to include the potential for sophisticated cyber attacks, which can adversely impact ICS operation, resulting in service interruption, equipment damage, safety concerns, and associated financial implications. In this work, a novel cyber-physical security framework for ICSs is proposed, which incorporates an analytics tool for attack detection and executes a reliable estimation-based attack-resilient control policy, whenever an attack is detected. The proposed framework is adaptable to already implemented ICS and the stability and optimal performance of the controlled system under attack has been proved. The performance of the proposed framework is evaluated using a reduced order model of a real EMS site and simulated attacks.