Multilayer optimisation for day-ahead energy planning in microgrids

In the search for low carbon, reliable and affordable ways to provide electricity, an increased attention is going to the microgrid, a small-scale power system that uses a combination of energy generation and storage devices to serve local customers. The most promising feature of the microgrid is its flexibility to act as a standalone source of electricity for remote communities, and to be connected to the main power system, selling and purchasing power as required. Additionally, a microgrid can be considered as a coordinated system approach for incorporating intermittent renewable sources of energy. Microgrid customers can have power from their batteries or distributed generators, they can buy it from the utility grid, or they can reduce their consumption.When designing a new optimal planning tool for a microgrid, a major challenge (and opportunity) is to decide on what units to operate in order to meet the demand. The question is what mix will provide the performance needed at the lowest cost, or with the lowest possible emissions. Unfortunately, both objectives are often contradictory. Generally, low costs mean high emissions, and vice versa. A microgrid system operator may care more about achieving lower costs rather than lower emissions. Given the preferences, the operator needs to decide how to configure and operate the microgrid while satisfying all technical requirements, such as voltage stability and power balance. In order to control and manage the microgrid units in real-time while fully exploiting the benefit of long-term prediction, an off-line optimisation approach imposes itself to devise the online microgrid management. In this PhD thesis, an efficient multilayer control approach is developed which obtains a day-ahead unit commitment method to provide an economically and environmentally viable unit commitment (UC) that is physically feasible in terms of voltage violations. With the multilayer control approach, the future operational states of the controllable units within the microgrid are determined ahead of time. The proposed concept follows the idea of a day-ahead coordination including the unit commitment problem (scheduling layer), an off-line power flow calculation (executive layer) and a security check with feedback control (adjustment layer). Since the complete multilayer control concept works on a day-ahead time scale, the model can be considered as an off-line optimisation approach. The power reference set points provided by the multilayer control approach can, in turn, be used for an online microgrid implementation to achieve real-time system state updates

Techno-economic energy models for low carbon business parks

To mitigate climate change, global greenhouse gas emissions need to be reduced substantially. Industry and energy sector together are responsible for a major share of those emissions. Hence the development of low carbon business parks by maximising energy efficiency and changing to collective, renewable energy systems at local level holds a high reduction potential. Yet, there is no uniform approach to determine the optimal combination and operation of energy technologies composing such energy systems. However, techno-economic energy models, custom tailored for business parks, can offer a solution, as they identify the configuration and operation that provide an optimal trade-off between economic and environmental performances. However, models specifically developed for industrial park energy systems are not detected in literature, so identifying an existing model that can be adapted is an essential step. In this paper, energy model classifications are scanned for adequate model characteristics and accordingly, a confined number of models are selected and described. Subsequently, main model features are compared, a practical typology is proposed and applicability towards modelling industrial park energy systems is evaluated. Energy system evolution models offer the most perspective to compose a holistic, but simplified model, whereas advanced energy system integration models can adequately be employed to assess energy integration for business clusters up to entire industrial sites. Energy system simulation models, however, provide deeper insight in the system’s operation

Dynamic optimisation for environomic power dispatch in microgrids

As a result of the increasing number of distributed energy resources (DER) in the electrical grid and their commitment to future market participation, control strategies for the optimal operation of DER gain importance. For this scenario a microgrid is a promising approach and forms a solution to this challenge. Microgrids are subsystems of the distribution grid including distributed generation (DG) units, storage devices and controllable loads, and can operate either connected or isolated from the utility grid. Ensuring a smooth, reliable and economic operation of a microgrid requires an energy management system that dynamically fits the production to the consumption in combination with storage. Quick response of the energy management strategy is crucial for a microgrid as compared to a conventional energy system. In this paper, a formulation of the environomic power dispatch approach in microgrids is proposed which uses multiobjective optimisation. The application aims to fulfill the time varying energy demand while minimising the costs and emissions of the local production and imported energy from the utility grid. With the introduction of a storage device, stored energy is controlled to balance the power generation of renewable sources, cover the overall microgrid demand and to optimise the overall power exchange between utility grid and microgrid. Operational constraints such as generator limits, start-up, operation and maintenance costs and the intermittency of renewable energy sources (RES) are to be satisfied. A representative microgrid structure is studied as an example and some simulation results are presented to demonstrate the performance of the microgrid environomic power dispatch approach

Developing and testing power control for wind power model

This thesis investigates a power algorithm for small wind turbines without pitch control. This research suggests the possibility to produce the maximum power at every wind speed using an electrical braking torque. The set up of the research took place in a laboratory without real wind. Therefore, a hardware model of wind turbine, instead of a real wind turbine was required. A motor, controlled by a frequency converter and mechanically coupled to the shaft of the generator simulated the wind. A second frequency converter connected to the generator generated a braking torque. A third frequency converter delivered power to the grid. Using a PLC we a wind pattern could be integrated in order to simulate a real wind model with the motor. A power algorithm shows how we can respond to the tip speed of the blades with the braking torque in order to keep the maximum power coefficient

Multi-objective optimization for environomic scheduling in microgrids

Microgrids are small-scale power systems including distributed generation (DG) units, storage devices and controllable loads, and can operate either connected or isolated from the utility grid. Ensuring an efficient, reliable, economic and environmentally friendly microgrid operation, an environomic power dispatch system is needed. In this paper, a formulation of an environomic scheduling approach in microgrids is proposed using multi-objective decision making method. The application aims to fulfill the time-varying energy demand while minimizing the costs and emissions of the internal production and imported energy from the utility grid. Operational constraints such as generator limits, operation and maintenance costs and the intermittency renewable energy sources (RES) will be satisfied. A representative microgrid structure, including measurement data, is studied as an example and some simulation results are presented to demonstrate the performance of the environomic scheduling approach

Dynamic Optimisation For Power Dispatch In Microgrids

The increasing growth of non-dispatchable renewable energy sources (RES) such as solar and wind and the upcoming plug-in electric vehicles (PEVs) makes the secure power dispatch a challenging optimisation. In this paper, a coordinated power dispatch strategy in microgrids with a battery management system is proposed. The power dispatch strategy dynamically fits the power production to the power demand by appropriately dispatching the controllable distributed energy resources (DER). A strategy is presented where an active battery scheduling system includes the load and generation profiles of the microgrid and the electricity prices of the spot market in order to maximise the microgrid revenue

A microgrid multilayer control concept for optimal power scheduling and voltage control

In this paper, a novel multilayer control structure for microgrids is proposed. A scheduling layer comprehends the minimization of the microgrid operating costs together with the CO2 emissions produced and provides a sequence of power references for the next 24 hours. Subsequently, within the executive layer, an off line AC power flow calculation will be performed to obtain the initial values of the voltage magnitudes of the different microgrid buses. The adjustment layer, which is the scope of this paper, includes a control strategy to maintain the voltage in the network. The purpose of this third layer is to keep the voltage within a pre-specified tolerance band by adjusting the power provided by the microgrid distributed energy resources (DER). Depending on the voltage deviation, the location of the DER units in the network and their distance from the voltage deviation, an appropriate dynamic gain will be provided to the relevant DER units. The renewed settings are then fed back to the first layer, which performs a new optimization and redistributes the adjusted reference set points among the DER units. The performance and effectiveness of the proposed hierarchical multilayer control structure were evaluated and demonstrated by several case studies