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.
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 .
@cite_26 @cite_48 @cite_5 @cite_15 @cite_34 @cite_13 @cite_12
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Efficient and reliable demand side management techniques for community charging of plug-in hybrid electrical vehicles (PHEVs) and plug-in electrical vehicles (PEVs) are needed, as large numbers of these vehicles are being introduced to the power grid. To avoid overloads and maximize customer preferences in terms of time and cost of charging, a constrained nonlinear optimization problem can be formulated. In this paper, we have developed a novel cooperative distributed algorithm for charging control of PHEVs PEVs that solves the constrained nonlinear optimization problem using Karush-Kuhn-Tucker (KKT) conditions and consensus networks in a distributed fashion. In our design, the global optimal power allocation under all local and global constraints is reached through peer-to-peer coordination of charging stations. Therefore, the need for a central control unit is eliminated. In this way, single-node congestion is avoided when the size of the problem is increased and the system gains robustness against single-link node failures. Furthermore, via Monte Carlo simulations, we have demonstrated that the proposed distributed method is scalable with the number of charging points and returns solutions, which are comparable to centralized optimization algorithms with a maximum of 2 sub-optimality. Thus, the main advantages of our approach are eliminating the need for a central energy management coordination unit, gaining robustness against single-link node failures, and being scalable in terms of single-node computations. Optimizing shared vehicle systems (bike-sharing car-sharing ride-sharing) is more challenging compared to traditional resource allocation settings due to the presence of complex network externalities. In particular, changes in the demand supply at any location (via dynamic pricing, rebalancing of empty vehicles, etc.) affect future supply throughout the system within short timescales. Such externalities are well captured by steady-state Markovian models, which are therefore widely used to analyze and design shared vehicle systems. However, using such models to design pricing control policies is computationally difficult since the resulting optimization problems are high-dimensional and non-convex. To this end, we develop a general approximation framework for designing pricing policies in shared vehicle systems, based on a novel convex relaxation which we term elevated flow relaxation. Our approach provides the first efficient algorithms with rigorous approximation guarantees for a wide range of objective functions (throughput, revenue, welfare). For any shared vehicle system with @math stations and m vehicles, our framework provides a pricing policy with an approximation ratio of 1+(n-1) m. This guarantee is particularly meaningful when m n, the average number of vehicles per station is large, as is often the case in practice. Further, the simplicity of our approach allows us to extend it to more complex settings. Apart from pricing, shared vehicle systems enable other control levers for modulating demand and supply, e.g. rebalancing empty vehicles, redirecting riders to nearby vehicles, etc. Our approach yields efficient algorithms with the same approximation guarantees for all these problems, and in the process, obtains as special cases several existing heuristics and asymptotic guarantees. We also extend our approach to obtain bi-criterion guarantees in multi-objective settings; we illustrate this with the example of Ramsey pricing. From a technical perspective, our work develops a new approach for obtaining control policies with approximation guarantees in steady-state Markovian models. Our approach can be distilled into the following three-step program: (i) construct an upper bound via a relaxation to the original problem that encodes essential conservation laws of the system, (ii) identify a family of control policies inducing known steady-state distributions that achieve the value of the relaxed solution in an appropriate scaling limit (in our case, state-independent policies in the limit m++), and (iii) characterize the performance loss between the finite system (i.e. fixed m) and the scaling limit. This technique may be of independent interest for other settings. The democratization of Internet of Things and ubiquitous computing equips citizens with phenomenal new ways for online participation and decision-making in application domains of smart grids and smart cities. When agents autonomously self-determine the options from which they make choices, while these choices collectively have an overall system-wide impact, an optimal decision-making turns into a combinatorial optimization problem known to be NP-hard. This paper contributes a new generic self-adaptive learning algorithm for a fully decentralized combinatorial optimization: I-EPOS, the Iterative Economic Planning and Optimized Selections. In contrast to related algorithms that simply parallelize computations or big data and deep learning systems that often require personal data and overtake of control with implication on privacy-preservation and autonomy, I-EPOS relies on coordinated local decision-making via structured interactions over tree topologies that involve the exchange of entirely local and aggregated information. Strikingly, the cost-effectiveness of I-EPOS in regards to performance vs. computational and communication cost highly outperforms other related algorithms that involve non-local brute-force operations or exchange of full information. The algorithm is also evaluated using real-world data from two state-of-the-art pilot projects of participatory sharing economies: (i) energy management and (ii) bicycle sharing. The contribution of an I-EPOS open source software suite implemented as a paradigmatic artifact for community aspires to settle a knowledge exchange for the design of new algorithms and application scenarios of sharing economies towards highly participatory and sustainable digital societies. We describe algorithmic enhancements to a decision-support tool that residential consumers can utilize to optimize their acquisition of electrical energy services. The decision-support tool optimizes energy services provision by enabling end users to first assign values to desired energy services, and then scheduling their available distributed energy resources (DER) to maximize net benefits. We chose particle swarm optimization (PSO) to solve the corresponding optimization problem because of its straightforward implementation and demonstrated ability to generate near-optimal schedules within manageable computation times. We improve the basic formulation of cooperative PSO by introducing stochastic repulsion among the particles. The improved DER schedules are then used to investigate the potential consumer value added by coordinated DER scheduling. This is computed by comparing the end-user costs obtained with the enhanced algorithm simultaneously scheduling all DER, against the costs when each DER schedule is solved separately. This comparison enables the end users to determine whether their mix of energy service needs, available DER and electricity tariff arrangements might warrant solving the more complex coordinated scheduling problem, or instead, decomposing the problem into multiple simpler optimizations.