Participation in energy demand response programs requires an active role by users of residential appliances: they contribute flexibility in appliance usage as the means to adjust energy consumption and improve Smart Grid reliability. Understanding the collective potential that appliance-level flexibility has on Smart Grid reliability is challenging and complex. Physical characteristics of appliances, usage preferences, habits, and lifestyle are socio-technical factors that influence system-wide reliability coming often at the expense of users' comfort, i.e. thermal. This paper studies appliance-level flexible scheduling and specifically the following research questions: (i) How flexible are users in scheduling their appliances to improve Smart Grid reliability? (ii) How do users' comfort requirements affect the contributions of flexibility and as a result the collective action of improving Smart Grid reliability? (iii) Which appliances have the highest regulatory impact on Smart Grid? (iv) Can flexibility further improve Smart Grid reliability compared to simply operating individual appliances more efficiently? And finally, (v) what is the impact of varying users' participation on the collective action of improving reliability? To address these questions, a distributed optimisation scheme is studied to coordinate the selection of multiple appliance-level schedules representing users' self-determined flexibility. Experimental evaluation using a novel dataset shows that higher user flexibility significantly improves Smart Grid reliability with the oven having the highest system-wide potential for this. Compared to an existing efficiency scheme for kettles, flexible coordinated scheduling shows further improvements in reliability. These new findings have implications for the design of more cost-effective and granular demand response programs in participatory and decentralised Smart Grids.
@cite_26: Demand-side energy management improves robustness and efficiency in Smart Grids. Load-adjustment and load-shifting are performed to match demand to available supply. These operations come at a discomfort cost for consumers as their lifestyle is influenced when they adjust or shift in time their demand. Performance of demand-side energy management mainly concerns how robustness is maximized or discomfort is minimized. However, measuring and controlling the distribution of discomfort as perceived between different consumers provides an enriched notion of fairness in demand-side energy management that is missing in current approaches. This paper defines unfairness in demand-side energy management and shows how unfairness is measurable and controllable by software agents that plan energy demand in a decentralized fashion. Experimental evaluation using real demand and survey data from two operational Smart Grid projects confirms these findings. © 2014 IEEE.
@cite_48: The robustness of smart grids is challenged by unpredictable power peaks or temporal demand oscillations that can cause blackouts and increase supply costs. Planning of demand can mitigate these effects and increase robustness. However, the impact on consumers in regards to the discomfort they experience as a result of improving robustness is usually neglected. This paper introduces a decentralized agent-based approach that quantifies and manages the tradeoff between robustness and discomfort under demand planning. Eight selection functions of plans are experimentally evaluated using real data from two operational smart grids. These functions can provide different quality of service levels for demand-side energy self-management that capture both robustness and discomfort criteria.
@cite_5: The Internet of Things equips citizens with a phenomenal new means for online participation in sharing economies. When agents self-determine options from which they choose, for instance, their resource consumption and production, while these choices have a collective systemwide impact, optimal decision-making turns into a combinatorial optimization problem known as NP-hard. In such challenging computational problems, centrally managed (deep) learning systems often require personal data with implications on privacy and citizens’ autonomy. This article envisions an alternative unsupervised and decentralized collective learning approach that preserves privacy, autonomy, and participation of multi-agent systems self-organized into a hierarchical tree structure. Remote interactions orchestrate a highly efficient process for decentralized collective learning. This disruptive concept is realized by I-EPOS, the Iterative Economic Planning and Optimized Selections, accompanied by a paradigmatic software artifact. Strikingly, I-EPOS outperforms related algorithms that involve non-local brute-force operations or exchange full information. This article contributes new experimental findings about the influence of network topology and planning on learning efficiency as well as findings on techno-socio-economic tradeoffs and global optimality. Experimental evaluation with real-world data from energy and bike sharing pilots demonstrates the grand potential of collective learning to design ethically and socially responsible participatory sharing economies.
@cite_15: The penetration of electric vehicles becomes a catalyst for the sustainability of Smart Cities. However, unregulated battery charging remains a challenge causing high energy costs, power peaks or even blackouts. This paper studies this challenge from a socio-technical perspective: social dynamics such as the participation in demand-response programs, the discomfort experienced by alternative suggested vehicle usage times and even the fairness in terms of how equally discomfort is experienced among the population are highly intertwined with Smart Grid reliability. To address challenges of such a socio-technical nature, this paper introduces a fully decentralized and participatory learning mechanism for privacy-preserving coordinated charging control of electric vehicles that regulates three Smart Grid socio-technical aspects: (i) reliability, (ii) discomfort and (iii) fairness. In contrast to related work, a novel autonomous software agent exclusively uses local knowledge to generate energy demand plans for its vehicle that encode different battery charging regimes. Agents interact to learn and make collective decisions of which plan to execute so that power peaks and energy cost are reduced system-wide. Evaluation with real-world data confirms the improvement of drivers' comfort and fairness using the proposed planning method, while this improvement is assessed in terms of reliability and cost reduction under a varying number of participating vehicles. These findings have a significant relevance and impact for power utilities and system operator on designing more reliable and socially responsible Smart Grids with high penetration of electric vehicles.
@cite_34: The problem of optimally scheduling the charging demand of electric vehicles within the constraints of the electricity infrastructure is called the charge scheduling problem. The models of the charging speed, horizon, and charging demand determine the computational complexity of the charge scheduling problem. For about 20 variants the problem is either in P or weakly NP-hard and dynamic programs exist to compute optimal solutions. About 10 other variants of the problem are strongly NP-hard, presenting a potentially significant obstacle to their use in practical situations of scale.
@cite_13: Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. This paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.
@cite_12: This paper addresses the challenges of distributed multi-agent optimisation, in environments with potential adversarial agents. In distributed multi-agent optimisation, each agent has a local cost function and the collective goal is to optimise the global cost function, which takes as input, the output of each agent's local cost function. Such optimisation algorithms are used in different fields such as distributed machine learning, distributed robotics, and recently, distributed energy planning. A prominent assumption in most of these algorithms is that all the agents are cooperative, non-faulty, and non-adversarial. Yet, in scenarios with multi-agent systems, such assumptions are not always valid. Recently, there has been some research in the area of resilience and fault-tolerant distributed multi-agent optimisation. However, these studies either assume a fully Byzantine environment, or have their own adversary model with different sets of assumptions about adversary capabilities. This makes the analysis and comparison between the results challenging. This paper presents a review of such algorithms which helps in defining and investigating restricted adversarial behaviour in distributed multi-agent optimisation. In comparison to the Byzantine environments which carry no assumptions about the agent's capability or purpose, in this paper adversaries have restrictions in regard to their knowledge, capabilities, purpose, and impact. This approach makes it easier to provide guarantees about the system performance depending on the specific adversary model and avoid the overly conservative, 'one size fits all' algorithms needed in fully Byzantine environments. Moreover, using the gained insights, previously unexplored aspects of adversarial behaviour in distributed multi-agent optimisation are discovered and indicated as possible future research directions.
Finally, the complexity of coordination and optimal selection of users' schedules is combinatorial (NP-Hard) and computationally expensive @cite_34 . Distributed optimisation and control algorithms have been utilised in Smart Grids and demand response program to cope with this complexity. Such algorithms approximate a near-optimal solution between users' demand and the supply requirements of the utility company @cite_13 . By utilising a distributed algorithm, the demand response program can better address the increasing penetration of distributed energy resources. It can also expand the possible solution space, improve the privacy of users, and provide more resilience against adversarial users @cite_12 . This paper utilises and expands I-EPOS to provide a distributed, privacy-preserving coordination and optimisation scheme for users' schedules @cite_5 . I-EPOS has been used in various sharing economy scenarios such as energy management @cite_5 @cite_26 @cite_48 , bike sharing @cite_5 , and charging control of electric vehicles @cite_15 .