Distributed mode scheduling for coordinated power balancing

Kawashima, Hiroyuki; Kato, T.; Matsuyama, Takashi

doi:10.1109/smartgridcomm.2013.6687927

Cited by 6 publications

(20 citation statements)

References 15 publications

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“…Coordinated energy management was proposed in [5], extended in [4], and its concept described in [6]. It belongs to the demand management from the demand-side category, but differs from other methods in this category in the problem it tries to solve and in the management approach.…”

Section: Coordinated Energy Managementmentioning

confidence: 99%

“…The cost function f i (x i ) of agent i can measure QoL, economic cost/benefit, and physical constraints associated x i , while the cost function g(∑ i∈N x i ) can measure economic cost/benefit, constraints, and flatness associated to the aggregated profile ∑ i∈N x i . Figure 3a presents a scheme of the coordination and the involved functions, and Figure 3b illustrates the profile-based cost functions (see [5] for agent cost functions and examples). To coordinate the power usage taking into account the agents' and the global cost functions, the community solves the following optimization problem (known as the sharing problem):…”

Section: Day-ahead Power Consumption Coordination Formulationmentioning

confidence: 99%

“…To solve problem (P1), the protocol introduced in [5] is used (extensions of this protocol have been presented in [4,31,32]). This protocol is based on the Alternating Direction Method of Multipliers (ADMM) [33], and it iteratively solves Problem (P1):…”

Section: Distributed Coordination Protocolmentioning

confidence: 99%

“…[4,5]). For the agents, we consider a simplified model to illustrate the benefits of the coordination (see [5] for a generative probabilistic model of agents/appliances that can be built using machine learning methods). The dissatisfaction of agent i for selecting a profile x i is measured by:…”

Section: Coordinated Energy Managementmentioning

confidence: 99%

“…Seeking to address the issues discussed above, in this work we augment the demand management proposed in [4,5] using the ideas on demand management introduced in [3] and the ideas on coordinated energy management of prosumer communities outlined in [6]. For this, we propose an augmented power consumption management model that considers uncontrolled power consumption and generation, day-ahead controllable power, and real-time controllable power.…”

Section: Introductionmentioning

confidence: 99%

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Energy Management in Prosumer Communities: A Coordinated Approach

2016

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Abstract:The introduction of uncontrollable renewable energy is having a positive impact on our health, the climate, and the economy, but it is also pushing the limits of the power system. The main reason for this is that, in any power system, the generation and consumption must match each other at all times. Thus, if we want to further introduce uncontrollable generation, we need a large ability to manage the demand. However, the ability to control the power consumption of existing demand management approaches is limited, and most of these approaches cannot contribute to the introduction of reneweables, because they do not consider distributed uncontrolled consumption and generation in the control. Furthermore, these methods do not allow users to exchange or jointly manage their power generation and consumption. In this context, we propose an augmented energy management model for prosumers (i.e., producer and consumer). This model considers controlled and uncontrolled generation and consumption, as well as the prosumer's ability (i) to plan the intended power consumption; and (ii) to manage real-time deviations from the intended consumption. We apply this model to the energy management of prosumer communities, by allowing the prosumers to coordinate their power consumption plan, to manage the deviations from the intended consumption, and to help each other by compensating deviations. The proposed approach seeks to enhance the power system, and to enable a prosumer society that takes account social and environmental issues, as well as each prosumer's quality of life.

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Section: Coordinated Energy Managementmentioning

confidence: 99%

Section: Day-ahead Power Consumption Coordination Formulationmentioning

confidence: 99%

Section: Distributed Coordination Protocolmentioning

confidence: 99%

Section: Coordinated Energy Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy Management in Prosumer Communities: A Coordinated Approach

2016

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Coordinated energy management for inter-community imbalance minimization

et al. 2016

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a b s t r a c tWith the increase in demand flexibility and the rapid introduction of uncontrollable renewable power sources, effective schemes for managing the power usage of end-users are required. In this line, we propose an energy management framework for coordinating the power usage of communities of networked agents. More specifically, communities (groups of loads) coordinate to minimize their aggregated power imbalance, while taking into account each community's objectives and constraints, as well as the preferred power usage pattern of each end-user. For having a robust coordination that can work under unexpected events, we propose to assign the agents to communities using a measure of the flexibility of sets of agents. The coordination framework builds on the alternating directions method of multipliers (ADMM), algorithm that is used to implement a distributed coordination using a hierarchical architecture. While the distributed coordination allows to manage the power usage of each end-user, the hierarchical architecture enables the integration, in a single framework, of energy management problems that would be otherwise handled independently. We illustrate and analyze the coordination framework using three simulated scenarios.

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A distributed coordination framework for on-line scheduling and power demand balancing of households communities

Verschae

Kawashima

Kato

et al. 2014

2014 European Control Conference (ECC)

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The development of energy management systems (EMS) has attracted increasing attention during the last years. One of the main goals of EMS is to balance the power usage and generation, while also maintaining the quality of life (QoL) of the users. In this paper, a distributed coordination framework for on-line scheduling of appliances and control of the aggregated power demand of households is proposed. Each household consists of a set of appliances, and each appliance is modeled using a probabilistic generative model of its power usage profile, which can be updated through the day to indicate changes in user preferences. The coordination framework is formulated as a receding horizon distributed optimization, where the households' QoL, and the deviation from a scheduled usage are taken into account. The implemented distributed optimization can be seen as a negotiation among the households and a coordinator: the coordinator seeks to balance the aggregated power consumption by minimizing the deviation from a scheduled aggregated power usage, while each household tries not to deviate much from its preferred usage pattern.1 A household is understood as any group of appliances that can be controlled (not only at homes), such as offices, buildings, etc.

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Distributed mode scheduling for coordinated power balancing

Cited by 6 publications

References 15 publications

Energy Management in Prosumer Communities: A Coordinated Approach

Energy Management in Prosumer Communities: A Coordinated Approach

Coordinated energy management for inter-community imbalance minimization

A distributed coordination framework for on-line scheduling and power demand balancing of households communities

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