Decentralized Planning of Energy Demand for the Management of Robustness and Discomfort

Pournaras, Evangelos; Vasirani, Matteo; Kooij, Robert E.; Aberer, Karl

doi:10.1109/tii.2014.2332114

Cited by 38 publications

(42 citation statements)

References 23 publications

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“…Each agent i selects one and only one plan d i,j = (d i,j,t ) T t=1 to execute according to a selection function j = f s (D i ) ∈ {1, ..., v} designed to serve a system-wide objective such as the improvement of reliability or the reduction of power cost in the Smart Grid. Reliability concerns how homogeneous the total power demand is over time so that power peaks causing cascading failures [32,53] are prevented or mitigated. Cost concerns the monetary value of the total power demand governed by the spot price signal P = (p t ) T t=1 (USD/kWh) of a power market [54].…”

Section: An Overviewmentioning

confidence: 99%

Socio-technical smart grid optimization via decentralized charge control of electric vehicles

Pournaras

Jung

Yadhunathan

et al. 2019

Applied Soft Computing

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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 1 Stampfenbachstrasse 48, 8092,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.

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Section: An Overviewmentioning

confidence: 99%

Socio-technical smart grid optimization via decentralized charge control of electric vehicles

Pournaras

Jung

Yadhunathan

et al. 2019

Applied Soft Computing

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“…A tree overlay network provides a cost-effective aggregation of the demand level required for coordinating the decision-making: each agent generates a number of transactive signals p referred to as possible plans D i = (d i, j ) p j=1 and collectively selects the plan d i,s that fits best to an objective. Decisionmaking is performed with selection functions [32] that evaluate characteristics of the demand plan, e.g. how uniformly distributed demand is over time, how large the demand peaks are, etc.…”

Section: Self-regulationmentioning

confidence: 99%

Self-regulating supply–demand systems

Pournaras

Yao²,

Helbing³

2017

Future Generation Computer Systems

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Supply-demand systems in Smart City sectors such as energy, transportation, telecommunication, are subject of unprecedented technological transformations by the Internet of Things. Usually, supply-demand systems involve actors that produce and consume resources, e.g. energy, and they are regulated such that supply meets demand, or demand meets available supply. Mismatches of supply and demand may increase operational costs, can cause catastrophic damage in infrastructure, for instance power blackouts, and may even lead to social unrest and security threats. Long-term, operationally offline and top-down regulatory decision-making by governmental officers, policy makers or system operators may turn out to be ineffective for matching supply-demand under new dynamics and opportunities that Internet of Things technologies bring to supply-demand systems, for instance, interactive cyberphysical systems and software agents running locally in physical assets to monitor and apply automated control actions in real-time. e.g. power flow redistributions by smart transformers to improve the Smart Grid reliability. Existing work on online regulatory mechanisms of matching supply-demand either focuses on game-theoretic solutions with assumptions that cannot be easily met in real-world systems or assume centralized management entities and local access to global information. This paper contributes a generic decentralized self-regulatory framework, which, in contrast to related work, is shaped around standardized control system concepts and Internet of Things technologies for an easier adoption and applicability. The framework involves a decentralized combinatorial optimization mechanism that matches supply-demand under different regulatory scenarios. An evaluation methodology, integrated within this framework, is introduced that allows the systematic assessment of optimality and system constraints, resulting in more informative and meaningful comparisons of self-regulatory settings. Evidence using real-world datasets of energy supply-demand systems confirms the effectiveness and applicability of the self-regulatory framework. It is shown that a higher informational diversity in the options, from which agents make local selections, results in a higher system-wide performance. Several strategies with which agents make selections come along with measurable performance trade-offs creating a vast potential for online adjustments incentivized by utilities, system operators and policy makers.

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“…In other words, the solutions have the maximum possible tolerance against uncertainty while providing the desired performance [9]. Robust frameworks [10] can be categorized into three types [11]. In Type I, worst-case uncertainty scenarios are incorporated into the problem.…”

Section: Indicesmentioning

confidence: 99%

Reliability‐based model for generation and transmission expansion planning

Ahmadi

Mavalizadeh

Zobaa

et al. 2017

IET Generation, Transmission & Distribution

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This paper presents a mixed integer linear multi-objective model based on information gap decision theory (IGDT), which is used to solve coordinated multiyear generation and transmission expansion planning (G&TEP) problems. The model maximizes the robustness of each uncertain parameter while a maximum allowable budget range is set. Fuel transportation price is considered. The results provide a numerical tool for system planner to help him adjust the appropriate level of robustness for each uncertain parameter of the problem. Extra limits on security, gaseous emission, and fuel availability are considered. A multi-objective method called the ε-constraint method is used here to maximize the robust region of load and investment costs simultaneously. The model is implemented on a six-bus Garver test system and 24 bus IEEE test system. The numerical results show the good performance of the model.

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Decentralized Planning of Energy Demand for the Management of Robustness and Discomfort

Cited by 38 publications

References 23 publications

Socio-technical smart grid optimization via decentralized charge control of electric vehicles

Socio-technical smart grid optimization via decentralized charge control of electric vehicles

Self-regulating supply–demand systems

Reliability‐based model for generation and transmission expansion planning

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