Distributed model predictive control of energy systems in microgrids

Stadler, Paul; Ashouri, Araz; Maréchal, François

doi:10.1109/syscon.2016.7490607

Cited by 22 publications

(22 citation statements)

References 17 publications

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“…subject to the dynamics (11). The consistency constraints (13) are adjoined to (17) by means of the multipliers defined in (16), where ii and i j are associated with (13a) and (13b), respectively. The consistency constraints are additionally penalized in (17) with parameter > 0, as typically done in augmented Lagrangian formulations.…”

Section: Figurementioning

confidence: 99%

“…The explicit computations of the local variables z q i according to (21) directly follows from the analytic solution of the minimization problem (19b), as mentioned before. The multiplier update in Step 3) computes q i as defined in (16). Moreover, the additional step (23) generates local copies of the multipliers q i associated with the neighboring MPC agents ∈  i→ in order to avoid an additional communication step.…”

Section: Distributed Solution By Admmmentioning

confidence: 99%

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Distributed model predictive control for continuous‐time nonlinear systems based on suboptimal ADMM

Bestler

Graichen

2018

Optim Control Appl Methods

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Summary This paper presents a distributed model predictive control (DMPC) scheme for continuous‐time nonlinear systems based on the alternating direction method of multipliers (ADMM). A stopping criterion in the ADMM algorithm limits the iterations and therefore the required communication effort during the DMPC solution at the expense of a suboptimal solution. Stability results are presented for the suboptimal DMPC scheme under two different ADMM convergence assumptions. In particular, it is shown that the required iterations in each ADMM step are bounded, which is also confirmed in simulation studies.

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Section: Figurementioning

confidence: 99%

Section: Distributed Solution By Admmmentioning

confidence: 99%

Distributed model predictive control for continuous‐time nonlinear systems based on suboptimal ADMM

Bestler

Graichen

2018

Optim Control Appl Methods

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“…More specifically, some research lines attack the problem of wisely operating (B)ESS (Battery Energy Storage System) and DERs (Distributed Energy Resources) and modifying pre-scheduled consumption profile of flexible loads, possibly involving end-users in the decisionmaking process. In particular, [2,[11][12][13] and [14] discussed the role of the (flexible) load in supporting the grid ancillary services and frequency regulation. Others, like [15], [16], focus on economic or operation optimization of microgrid.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is well-known that this scheme presents issues of scalability, computational burden, failure of single unit, adaptability, etc. Recent works are putting more attention to the distributed MPC and hierarchical control schemes, such as [11], [19]. In particular, in [19], a two-layer control scheme based MPC operating at two different timescales has been studied.…”

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confidence: 99%

A hierarchical distributed predictive control approach for microgrids energy management

Dao

Dehghani-Pilehvarani

Markou

et al. 2019

Sustainable Cities and Society

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This paper addresses the problem of management and coordination of energy resources in a typical microgrid, including smart buildings as flexible loads, energy storages and renewables. The overall goal is to provide a comprehensive and innovative framework to maximize the overall benefit, still accounting for possible requests to change the load profile coming from the grid and leaving every single building or user to balance between servicing those requests and satisfying his own comfort levels. The user involvement in the decision-making process is granted by a management and control solution exploiting an innovative distributed model predictive control approach with coordination. In addition, also a hierarchical structure is proposed, to integrate the distributed MPC user-side with the microgrid control, also implemented with an MPC technique. The proposed overall approach has been implemented and tested in several experiments in the laboratory facility for distributed energy systems (Smart RUE) at NTUA, Athens, Greece. Simulation analysis and results complement the testing, showing the accuracy and the potential of the method, also in the perspective of implementation. Index Terms-Energy Management, Distributed Control, Model Predictive Control, Microgrid(MPC), which is well suited to deal with a large amount of constraints of different types that have to be imposed in real time in microgrids. This technique has been exploited for example in [2,3,11,12] and [17][18][19][20][21][22][23]. In [2], [3] and [21] the flexible loads are modelled as a predefined range of possible load consumption and the microgrid controller can directly determine a load profile as long as it fulfils the range. This approach neglects the dynamic of load flexibility in the shiftable loads category (e.g., heating/cooling system, refrigerator, washing machine, etc.) since the size of the predefined range in one step of this load type can not be fixed in advance and it depends on the value of decision variable in the previous steps. On the other hand, in [17] and [23], the load side may reveal its model which later will be formulated in the optimization problem in a centralized controller.Concerning the control scheme, a centralized predictive scheme is considered in [2], [3], [12], [17] and [18]. However, it is well-known that this scheme presents issues of scalability, computational burden, failure of single unit, adaptability, etc. Recent works are putting more attention to the distributed MPC and hierarchical control schemes, such as [11], [19]. In particular, in [19], a two-layer control scheme based MPC operating at two different timescales has been studied. In the paper, some details on the markets (e.g., imbalance charge, difference in purchasing and selling tariffs) are neglected and the flexible load is not considered. On the other hand, the paper [11] employs a sequential distributed MPC on energy management problems in the microgrid; however, some details are missing, including the presence of RES (Renewable Energy Resource),...

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“…As a consequence, for example, research was performed to estimate the value of the flexibility of multiple aggregated prosumers for the spot market under given bidding rules [12]. Other studies have dealt with the development of control schemes to optimize local energy management considering possibilities for market participation [13,14], stochastic market prices [15], and locally distributed intelligence for grid congestion and management [16]. All of these studies indicate a large potential of the local flexibility.…”

Section: Introductionmentioning

confidence: 99%

Monetary Value of a District’s Flexibility on the Spot- and Reserve Electricity Markets

Facchinetti

Rohrbach

Wel³

et al. 2018

Buildings

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In the future, advanced multi-energy systems are expected to handle an increasing share of fluctuating renewable energy generation through the management of multiple advanced energy conversion and storage technologies operating across different energy carriers. The market diffusion of such concepts of Local Energy Management—the management of energy supply, demand, and storage within a given geographical area—is expected to provoke a fundamental reorganization of the power generation sector. This work contributes to this topic by estimating the maximum potential economic value attained from using the flexibility of a district to take advantage of operating within multiple electricity markets at the same time. The study is based on the measured demand and production data of a newly built suburban residential district located in Central Switzerland. The actual configuration of the district and the resulting flexibility, as well as an extension with a battery storage system, is used to estimate the economic value of the flexibility. Then, an optimization algorithm manages flexible demand, production, and storage capacities in order to alternatively maximize the revenues/cost savings, self-sufficiency, or share of renewable resources of the district’s energy supply. In this vein, the impact of the way the system operates in the markets regarding the degradation of the battery is assessed and its pay-back-time is estimated. The analysis revealed a considerable profit potential associated with the district thermal and electricity storage flexibility, in particular, when operating on both the spot and reserve electricity markets. Firstly, it was shown that overall energy costs can be minimized through an optimal management of energy conversion and storage systems. Secondly, complementing the infrastructure with batteries and trading flexibility on the spot market would decrease costs by about 43%, while an additional 20% cost decrease could be captured by including trading on the reserve market. Thirdly, it has been shown that operation on the spot- and reserve market does not seem to degrade the battery more than solely operation on the spot market. However, when operating on the spot- and reserve markets, battery amortization would still take about 10 years.

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Distributed model predictive control of energy systems in microgrids

Cited by 22 publications

References 17 publications

Distributed model predictive control for continuous‐time nonlinear systems based on suboptimal ADMM

Distributed model predictive control for continuous‐time nonlinear systems based on suboptimal ADMM

A hierarchical distributed predictive control approach for microgrids energy management

Monetary Value of a District’s Flexibility on the Spot- and Reserve Electricity Markets

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