Distributed MPC for controlling mu-CHPs in a network

This paper describes a dynamic price mechanism to coordinate electricity generation from micro Combined Heat and Power (mu-CHP) systems in a network of households. The control is done on household level in a completely distributed manner. Distributed Model Predictive control is applied to the network of households with mu-CHP installed. Each house has a unique demand pattern based on realistic data. Information from a few neighbors are taken into account in the local optimal control problems. Desired behavior for the network model in the distributed MPC approach is showed by simulation

Distributed MPC for controlling μ-CHPs in a network

This paper describes a dynamic price mechanism to coordinate electricity generation from micro Combined Heat and Power (μ-CHP) systems in a network of households. The control is done on household level in a completely distributed manner. Distributed Model Predictive control is applied to the network of households with μ-CHP installed. Each house has a unique demand pattern based on realistic data. Information from a few neighbors are taken into account in the local optimal control problems. Desired behavior for the network model in the distributed MPC approach is showed by simulation.</p

Distributed MPC for controlling μ-CHPs in a network

This paper describes a dynamic price mechanism to coordinate electricity generation from micro Combined Heat and Power (μ-CHP) systems in a network of households. The control is done on household level in a completely distributed manner. Distributed Model Predictive control is applied to the network of households with μ-CHP installed. Each house has a unique demand pattern based on realistic data. Information from a few neighbors are taken into account in the local optimal control problems. Desired behavior for the network model in the distributed MPC approach is showed by simulation.</p

Optimal control in a micro grid of households equipped with μ-CHPs and energy storage devices

This work studies optimal flow control of a micro grid consisting of households equipped with μ-CHP devices and gas and heat buffers. Agricultural wastes from households are used to produce biogas by a biogas generator. The produced biogas is, then, utilized to fulfill local demand of heat and power of the households. Excess biogas can be upgraded and sold to the low pressure gas grid. Excess electricity produced by the μ- CHPs of households can be also sold to the electricity grid. The aim of the control process is to maximize the estimated profit of the households while avoiding overloading gas and electricity grids and avoiding the biogas shortage. The decisions on the supply and consumption levels are done in both centralized and distributed fashions using model predictive control (MPC). The distributed MPC (dMPC) is developed from the centralized MPC (cMPC) by employing dual decomposition method combined with the projected sub-gradient method. In dMPC, each household makes decisions based on its local information, yet still needs to coordinate its supply and consumption bids to the grid operators and the biogas generator. The coordinations are formulated for synchronous and asynchronous implementations. With the distributed scheme, the grid operators and the biogas producer can manage households’ supply and consumption levels via dynamic pricing to obey the grid capacity constraints. We perform extensive simulations to investigate the behavior of dynamic pricing modified by the grid operators and the biogas generator. Furthermore, we provide numerical results to compare the performance of cMPC, synchronous dMPC, and asynchronous dMPC using realistic estimates of the selling prices and demand patterns

Supervisory model predictive control of building integrated renewable and low carbon energy systems

To reduce fossil fuel consumption and carbon emission in the building sector, renewable and low carbon energy technologies are integrated in building energy systems to supply all or part of the building energy demand. In this research, an optimal supervisory controller is designed to optimize the operational cost and the CO2 emission of the integrated energy systems. For this purpose, the building energy system is defined and its boundary, components (subsystems), inputs and outputs are identified. Then a mathematical model of the components is obtained. For mathematical modelling of the energy system, a unified modelling method is used. With this method, many different building energy systems can be modelled uniformly. Two approaches are used; multi-period optimization and hybrid model predictive control. In both approaches the optimization problem is deterministic, so that at each time step the energy consumption of the building, and the available renewable energy are perfectly predicted for the prediction horizon. The controller is simulated in three different applications. In the first application the controller is used for a system consisting of a micro-combined heat and power system with an auxiliary boiler and a hot water storage tank. In this application the controller reduces the operational cost and CO2 emission by 7.31 percent and 5.19 percent respectively, with respect to the heat led operation. In the second application the controller is used to control a farm electrification system consisting of PV panels, a diesel generator and a battery bank. In this application the operational cost with respect to the common load following strategy is reduced by 3.8 percent. In the third application the controller is used to control a hybrid off-grid power system consisting of PV panels, a battery bank, an electrolyzer, a hydrogen storage tank and a fuel cell. In this application the controller maximizes the total stored energies in the battery bank and the hydrogen storage tank

Central model predictive control of a group of domestic heat pumps, case study for a small district

In this paper we investigate optimal control of a group of heat pumps. Each heat pump provides space heating and domestic hot water to a single household. Besides a heat pump, each house has a buffer for domestic hot water and a floor heating system for space heating. The paper describes models and algorithms used for the prediction and planning steps in order to obtain a planning for the heat pumps. The optimization algorithm minimizes the maximum peak electricity demand of the district. Simulated results demonstrate the resulting aggregated electricity demand, the obtained thermal comfort and the state of charge of the domestic hot water storage for an example house. Our results show that a model predictive control outperforms conventional control of individual heat pumps based on feedback control principles

Toward optimal operation of multienergy home-microgrids for power balancing in distribution networks: a model predictive control approach

The energy policy objectives of the German government regarding renewable energy sources and energy efficiency will lead to a significantly increase in the share of photovoltaics, storage systems, CHP plants, and heat pumps, especially at the distribution grid level. In the future, inside a household, such systems must be coordinated in such a way that they can respond to variable network conditions as a single flexible unit. This dissertation defines home-microgrid as a residential building with integrated distributed energy resources, and follows a bottom-up approach, based on the cellular approach, which aims at improving local balancing in low-voltage grids by using the flexibilities of home-microgrids. For this purpose, the dissertation develops optimization-based strategies for the coordination of multienergy home-microgrids, focusing on the use of model predictive control. The main core of the work is the formulation of the underlying optimization problems and the investigation of coordination strategies for interconnected home-microgrids. In this context, the work presents the use of the dual decomposition and the alternating direction method of multipliers for hierarchical-distributed coordination strategies. Finally, this dissertation introduces a framework for the co-simulation of electrical networks with penetration of multienergy home-microgrids