This paper considers hydrogen and renewable electricity from power-to-gas (PtG) facilities supplied to a gas grid, a mobility sector, and a power grid. The PtG facilities are equipped with hydrogen buffers and fuel cells. The goal is to maximize the expected profit of PtG facilities without exceeding the grid capacities. The decisions on the supply levels are done in a distributed fashion, yet some coordination with the multiple energy grid operators is necessary to obey the grid capacity constraints. The distributed supply coordination is developed using a model predictive control approach based on a dual decomposition combined with the projected gradient method. The approach results in a two-layer optimization problem experienced by each PtG facility and each grid operator. In this way, the grid operators can manage the PtG facilities via dynamic distribution pricing. We study the fairness of the corresponding algorithm and discuss its practical implementation. Simulation results are provided to evaluate the algorithm performance and to investigate the evolution of distribution prices.

In this paper we study the coordination between biogas producers who can either use the biogas themselves, exchange biogas with their neighbors, or deliver it to the various energy grids, such as the low pressure gas grid or the power grid. These producers are called prosumers. In this setting gas storage, fuel cells, micro combined heat power systems, and heat buffers are all part of the prosumers' node. We aim to optimize the imbalance, profit, and comfort levels per prosumer, while taking the constraints of the energy grids into account, and while allowing prosumers to exchange energy with each other. This results in a two-layer optimization problem formulation. In addition, in practice, communication between prosumers among each other and with grid operators is done in an asynchronous manner. In this paper we study the problem of two-layer optimization for biogas prosumers embedded in multiple energy grids, while the (bidirectional) communication between the various partners is done asynchronously. We prove the convergence of the asynchronous coordination algorithm that uses both the inputs and the states. We conduct simulations for the biogas prosumer setting, using realistic data to illustrate the convergence of the algorithm and to study its practical implementation. Note to Practitioners-This paper is motivated and supported by a smart gas grid project of the Energy Delta Gas Research (EDGaR) consortium in the Netherlands. The project deals with investigating the capacity of smart grid technologies to facilitate the introduction of new gases into the distribution grids, with diverse gas qualities and multiple injection points. The gas distribution grid will have to move from a passive to an active distribution system that dynamically control bidirectional flows between end-users and the grid operators. As the endusers may be equipped with energy converters, other energy distribution grids also need to transform to active distribution systems. Existing approaches are distributed, where each enduser and energy grid operator can locally solve their optimal control problem. In this paper, we consider the fact that both endusers and grid operators do not have access to a common clock when solving their problem and when sharing their information. The information includes some of their states and controllable inputs. The asynchronous information exchange problem was pointed out by DNV GL Netherlands, Gasunie, and Gasterra which are companies we collaborate with within the EDGaR consortium. It is highly relevant for practical implementation of our distributed algorithms. In future research, we will include practical control considerations due to on-off constraints of

No abstract

Distributed asynchronous supply coordination for energy producers embedded in the energy grids Alkano, Desti; Scherpen, Jacquelien M.A.; Cao, Ming 5239). IEEE (The Institute of Electrical and Electronics Engineers).Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum. Download date: 02-04-2019Distributed asynchronous supply coordination for energy producers embedded in the energy grids Desti Alkano, Jacquelien M.A. Scherpen, and Ming CaoAbstract-This paper studies the congestion control and energy flow allocation of renewable energy producers equipped with local energy storage devices and energy converters. The producers are embedded in the existing energy grids. Based on the producers' own measurements and some coordination with the grid operators, the energy producers adjust locally their supply levels injected to the energy grids so as to maximize their profit without exceeding the grid capacities. We incorporate an asynchronous implementation in the distributed supply coordination and prove its convergence. We implement the proposed algorithm for Power-to-Gas facilities embedded in the energy grids, which consist of a gas grid, mobility sector, and power grid, to demonstrate that the distributed asynchronous supply coordination achieves the same optimal performances as those of the synchronized distributed supply coordination.

This chapter addresses the balancing problem that arises in smart energy grids. Because power generation from renewable energy resources is tied to environmental factors, supply is often fluctuating and decentralised. Minimising the imbalance between supply and demand is important for grid stability, as well as for economic considerations. Flexible appliances propose a means to achieve supply-demand matching by shifting their production or consumption in time. We take a distributed optimal control point of view: we formulate the problem as an optimal control problem and suggest solutions based on distributed model predictive control (MPC) methods. In particular, we aim to minimise the imbalance using demand response regulation and via Power-to-Gas facilities that offer energy storage. Furthermore, we discuss how demand response regulation can be embedded in the market structure of the Universal Smart Energy Framework. We present example simulations to demonstrate the viability of our approaches.