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.

Due to the increased use of variable renewable energy sources, more capacity for reserves is required. Non-generating resources such as large industrial consumers can arbitrage energy prices and provide reserve capacity by exploiting the inherent flexibility in selected industrial processes. A large enough industrial consumer can capitalize on this flexibility through optimized bidding in electricity markets. In this work, the day-ahead cost minimization of a risk-averse pulp and paper mill is formulated as a two-stage stochastic problem, considering thermodynamic and electrical constraints. The bids in the energy and reserve markets are jointly optimized subject to price uncertainty as well as uncertainty of frequency realization. The results of a case study in Sweden display a significant economic benefit in exploiting the flexibility of integrated pulp and paper mills with electric boilers. The expected cost of the pulp and paper mill resulting from different strategies are compared and the risk-aversion of the pulp and paper mill is investigated. Reserve offers are mainly facilitated by fast-acting electric boilers and supported by flexibility in the steam network. We show that reserve offers can significantly improve the profitability of the pulp and paper mill.

The home energy management problem has many different facets, including economic viability, data uncertainty and quality of strategy employed. The existing literature in this area focuses on individual aspects of this problem without a detailed, holistic analysis of the results with regards to practicality in implementation. In this paper, we fill this gap by performing a comprehensive comparison of seven different energy management strategies, each with different levels of practicality, sophistication and computational requirements. We analyse the results in the context of these three characteristics, and also critique the modelling assumptions made by each strategy. Our analysis finds that using a more sophisticated energy management strategy may not necessarily improve the performance and economic viability of the PV-battery system due to the effects of modelling assumptions, such as the treatment of uncertainties in the input data and battery degradation effects.

The electric power industry and society are facing challenges and opportunitiesof transforming the present power grid into a smart grid. Energymanagement systems (EMSs) play an important role in smart grids. A generalhierarchical structure for EMSs is considered here, which is composed ofa lower layer and an upper layer.

The first research objective of the thesis is detailed modeling, schedulingand control of flexible loads at the lower layer of EMSs. To do this, a wellstudiedframework has been extended, which focuses on scheduling of staticloads and dynamic loads for home energy management systems (HEMSs).Then, a robust formulation of the framework is proposed, which takes theuser behavior uncertainty into account so that the cost of optimal schedulingof appliances is less sensitive to unpredictable changes in user preferences.Considering that the optimization algorithms in the proposed framework canbe computationally intensive, an efficient plug-and-play policy is proposedand validated through several simulation studies.

The second research objective is to predict, plan, and control the aggregatedflexible load at the upper layer. Here, an iterative distributed approachamong aggregator and HEMSs is designed, to maximize the aggregated profitmade out of the shared energy storage system, while technical and operationalconstraints are satisfied. In addition, a strategy is proposed for flexibilityprediction of aggregated heterogeneous thermostatically controlled loads ina single micro-community of households. Then, algorithms are designed forplanning and control of aggregated flexibility in several micro-communities,to be used for bidding in energy and reserve markets.

To meet these research objectives, the control systems in the hierarchicalEMSs are connected over IT infrastructures and are in interaction with endusers.While this is done to achieve economical and environmental goals,it also introduces new sources of uncertainty in the control loops. Thus,the third research objective is to design policies to make the EMSs resilientagainst uncertainties and cyber attacks. Here, the user behavior uncertaintyhas been modeled, and a robust formulation is designed so that the optimalsolution for scheduling of appliances is more resilient to the uncertainties. Inaddition, fault-tolerant control techniques have been applied to a hierarchicalEMS to mitigate cyber-physical attacks, with no need for major re-designof the local control loops in already existing EMSs. Moreover, stability andoptimal performance of the proposed attack-resilient control policy have been proven.

I samband med den pågående omvandlingen av nuvarande elsystem tillsmarta elnät finns både utmaningar och möjligheter för elkraftindustrin. Såkallade energihanteringssystem (EMS) spelar en viktig roll i smarta elnät. Härbehandlas en generell hierarkisk struktur för EMS, bestående av två lager, ettlägre och ett övre lager.

Det främsta målet i avhandlingen är detaljerad modellering, schemaläggningoch styrning av flexibla laster i det lägre lagret av EMS. Ett tidigarestuderat ramverk som fokuserar på schemaläggning av statiska och dynamiskalaster för hushållens energihanteringssystem (HEMS) har därför vidareutvecklats.Vidare föreslås en robust formulering av ramverket som tarhänsyn till användarens beteendeosäkerhet så att kostnaden för optimal schemaläggningav apparater blir mindre känslig för oförutsägbara förändringar ianvändarpreferenser. Eftersom att optimeringsalgoritmerna kan vara beräkningsintensivaföreslås och valideras en effektiv plug-and-play-metod genomflera simuleringsstudier.

Ett annat syfte har varit att förutsäga, planera och styra den aggregeradeflexibla lasten i det övre lagret i EMS. Därför har ett iterativt distribuerattillvägagångssätt för aggregat och HEMS utformats för att maximera vinstenfrån det delade energilagringssystemet, samtidigt som tekniska och operativabegränsningar uppfylls. Dessutom föreslås en strategi för att förutsägaflexibiliteten hos aggregerade heterogena termostatstyrda belastningar i ettmikrosamhälle bestående av flera hushåll. Vidare utformas algoritmer för planeringoch kontroll av aggregerad flexibilitet i flera mikrosamhällen, som kananvändas för att delta på energi- och reservmarknader.

För att möta dessa forskningsmål kopplas styrsystemen i de hierarkiskaEMS-systemen ihop över IT-infrastruktur och samverkar med slutanvändare. Detta görs för att uppnå ekonomiska och miljömässiga mål, men kan ocksåskapa nya källor till osäkerhet i kontrollslingorna. Det tredje forskningsmåletär således att utforma metoder för att göra EMS motståndskraftiga motosäkerheter och cyberattacker. Här har osäkerheter i användarbeteenden modelleratsoch en robust formulering utformats för att göra schemaläggningav apparater mer motståndskraftig mot osäkerhet. Dessutom har feltolerantakontrolltekniker applicerats på en hierarkisk EMS för att mildra cyber-fysiskaattacker, utan att det behövs någon större förändring av de lokala kontrollslingornai redan befintliga EMS. Vidare har stabilitet och optimal prestandaför den föreslagna attackmotståndskraftiga kontrolltekniken bevisats.

Due to increased use of variable renewable energy sources, more capacity for reserves is required. Non-generating resources such as large industrial consumers can arbitrage energy prices and provide reserve capacity by exploiting the inherent flexibility in selected industrial processes. A large enough industrial consumer can capitalize on this flexibility through optimized bidding in electricity markets. In this work, the day-ahead cost minimization of a risk-averse pulp and paper mill (PPM) is formulated as a two-stage stochastic problem, considering thermodynamic and electrical constraints of the PPM. The bids in the energy and reserve markets are optimized subject to price uncertainty. The results of a case study in Sweden display a significant economic benefit in exploiting the flexibility of PPM. The expected cost of the pulp and paper mill resulting from different strategies are compared and the risk adversity of the PPM is investigated. We show that reserve offers can significantly improve the profitability of the PPM.

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.

Distributed generation (DG) and especially grid-connected residential photovoltaic (PV) systems areemerging and high penetration levels of these can have an adverse impact on several low voltage (LV)distribution grids in terms of power quality and reliability. In order to reduce that effect in a cost-effectivemanner, the traditional distribution grid planning process is being reengineered by incorporating the gridcontrol operations and considering the uncertainties e.g., DG power, demand and urban/rural expansionplans. One of the challenges is to determine if the required technology deployment to operate the gridscan provide a better solution in terms of quality and cost than the traditional approach, which is prin-cipally based on cable reinforcement and change of transformers. In addition, if controllers were to bedeployed, it would be important to determine where they should be placed and at what stage of theexpansion planning, especially when the planning is assumed to be non-deterministic.Therefore, following this situation, in this paper we propose an optimal way to deploy and to operateutility’s controllable resources at the distribution grid and additionally we consider the uncertaintiesrelated to PV growth and distribution grid expansion. Thus, we include the non-deterministic multistageperspective to the controller placement problem. Furthermore, we perform a techno-economic analysis ofthe results and we show that an optimal controller placement allows removing the overvoltage problemsarising in the LV grid in a more cost-effective way compared to a typical traditional grid reinforcementapproach.

Forecasting of power consumption has been a topic of great interest for the stakeholders of electricity markets. It has an essential role in decision making, including purchasing and generating electric power, load switching, and demand side management. Different algorithms are tested and used for balancing the demand and supply of energy. This research work focuses on predicting power consumption using time series forecasting methods for the Île-de-France region with publicly available energy data from RTE, France. The two machine learning algorithms Support Vector Machine (SVM) and Recurrent Neural Network (RNN) are implemented and tested for their accuracy in predicting day-ahead half-hourly power consumption data. This paper provides brief insights on the algorithms used and further explains the data handling for its implementation. The Mean Absolute Percentage Error (MAPE) is used as the performance measure. The results indicate a higher accuracy of the RNN at the cost of longer computation times.

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.