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

Corrective receding horizon EV charge scheduling using short-term solar forecasting

Forecast errors can cause sub-optimal solutions in resource planning optimization, yet they are usually modeled simplistically by statistical models, causing unrealistic impacts on the optimal solutions. In this paper, realistic forecast errors are prescribed, and a corrective approach is proposed to mitigate the negative effects of day-ahead persistence forecast error by short-term forecasts from a state-of-the-art sky imager system. These forecasts preserve the spatiotemporal dependence structure of forecast errors avoiding statistical approximations. The performance of the proposed algorithm is tested on a receding horizon quadratic program developed for valley filling the midday net load depression through electric vehicle charging. Throughout one month of simulations the ability to flatten net load is assessed under practical forecast accuracy levels achievable from persistence, sky imager and perfect forecasts. Compared to using day-ahead persistence solar forecasts, the proposed corrective approach using sky imager forecasts delivers a 25% reduction in the standard deviation of the daily net load. It is demonstrated that correcting day-ahead forecasts in real time with more accurate short-term forecasts benefits the valley filling solution

An optimal energy management system for islanded Microgrids based on multi-period artificial bee colony combined with Markov Chain

The optimal operation programming of electrical systems through the minimization of the production cost and the market clearing price, as well as the better utilization of renewable energy resources, has attracted the attention of many researchers. To reach this aim, energy management systems (EMSs) have been studied in many research activities. Moreover, a demand response (DR) expands customer participation to power systems and results in a paradigm shift from conventional to interactive activities in power systems due to the progress of smart grid technology. Therefore, the modeling of a consumer characteristic in the DR is becoming a very important issue in these systems. The customer information as the registration and participation information of the DR is used to provide additional indexes for evaluating the customer response, such as consumer's information based on the offer priority, the DR magnitude, the duration, and the minimum cost of energy. In this paper, a multiperiod artificial bee colony optimization algorithm is implemented for economic dispatch considering generation, storage, and responsive load offers. The better performance of the proposed algorithm is shown in comparison with the modified conventional EMS, and its effectiveness is experimentally validated over a microgrid test bed. The obtained results show cost reduction (by around 30%), convergence speed increase, and the remarkable improvement of efficiency and accuracy under uncertain conditions. An artificial neural network combined with a Markov chain (ANN-MC) approach is used to predict nondispatchable power generation and load demand considering uncertainties. Furthermore, other capabilities such as extendibility, reliability, and flexibility are examined about the proposed approach

The real-time optimisation of DNO owned storage devices on the LV network for peak reduction

Energy storage is a potential alternative to conventional network reinforcementof the low voltage (LV) distribution network to ensure the grid’s infrastructure remainswithin its operating constraints. This paper presents a study on the control of such storagedevices, owned by distribution network operators. A deterministic model predictive control (MPC) controller and a stochastic receding horizon controller (SRHC) are presented, wherethe objective is to achieve the greatest peak reduction in demand, for a given storagedevice specification, taking into account the high level of uncertainty in the prediction of LV demand. The algorithms presented in this paper are compared to a standard set-pointcontroller and bench marked against a control algorithm with a perfect forecast. A specificcase study, using storage on the LV network, is presented, and the results of each algorithmare compared. A comprehensive analysis is then carried out simulating a large number of LV networks of varying numbers of households. The results show that the performance of each algorithm is dependent on the number of aggregated households. However, on a typical aggregation, the novel SRHC algorithm presented in this paper is shown to outperform each of the comparable storage control techniques

Optimal Operation Management of Grid-connected Microgrid Using Multi-Objective Group Search Optimization Algorithm

Utilizing distributed generations (DGs) near load points has introduced the concept of microgrid. However, stochastic nature of wind and solar power generation as well as electricity load makes it necessary to utilize an energy management system (EMS) to manage hourly power of microgrid and optimally supply the demand. As a result, this paper utilizes demand response program (DRP) and battery to tackle this difficulty. To do so, an incentive-based DRP has been utilized and the effects of applying DRP on microgrid EMS problem have been studied. The objective functions of microgrid EMS problem include the total cost and emission. These metrics are combined in a multi-objective formulation and solved by the proposed multi-objective group search optimization (MOGSO) algorithm. After obtaining Pareto fronts, the best compromise solution is determined by using fuzzy decision making (FDM) technique. Studies have been employed on a test microgrid composed of a wind turbine, photovoltaic, fuel cell, micro turbine and battery while it is connected to the upper-grid. Simulation results approve the efficiency of the proposed method in hourly operation management of microgrid components

Coordination of EVs Participation for Load Frequency Control in Isolated Microgrids

Increasing the penetration levels of renewable energy sources (RESs) in microgrids (MGs) may lead to frequency instability issues due to intermittent nature of RESs and low inertia of MG generating units. On the other hand, presence of electric vehicles (EVs), as new high-electricity- consuming appliances, can be a good opportunity to contribute in mitigating the frequency deviations and help the system stability. This paper proposes an optimal charging/discharging scheduling of EVs with the goal of improving frequency stability of MG during autonomous operating condition. To this end, an efficient approach is applied to reschedule the generating units considering the EVs owners’ behaviors. An EV power controller (EVPC) is also designed to determine charge and discharge process of EVs based on the forecasted day-ahead load and renewable generation profiles. The performance of the proposed strategy is tested in different operating scenarios and compared to those from non-optimized methodologies. Numerical simulations indicate that the MG performance improves considerably in terms of economy and stability using the proposed strategy

A novel real-time demand side management scheme for the addition of electrical vechicles to the future grid

Electric Vehicles (EVs) as the alternative to the current fossil fuel vehicles represent the most promising green approach to the electrification of a significant portion of the transportation sector. Taking the randomness of EVs’ charging/ discharging characteristics into consideration, a significant uncertainty will be added to the grid. Consequently, charging/discharging management of EVs in the presence of large scale intermittent Renewable Energy Resources is considered as the most significant challenge for the future smart grid. Tackling the challenges of stable operation, this thesis proposes a novel approach of micro-grid stability by exploiting the demand side management. In this context, a comprehensive interactive hierarchical based architecture for the electricity supply and demand interaction in a smart grid environment is proposed to encourage the high participation of residential customers in a new deregulated electricity market. A novel market-oriented energy imbalance management scheme is also proposed for the seamless integration of EVs to the grid in the presence of intermittent resources. The proposed scheme which, unlike previous works, utilizes the grid’s operating characteristics model within the signaling gametheoretic approach for the successful operation of electricity market. Optimal decision strategies for both EV owners and utility are devised by capturing the conflicting economic interests of players together under load/generation uncertainties. Thus, this thesis presents a planning tool for electric utilities that can provide an insight into the implementation of demand response at the end-user level in an automated way to bridge the gap between scheduling EVs and its benefits. The efficacy of the proposed approach in reducing peak loads while satisfying customers’ needs are demonstrated and compared with other schemes. Results show that the proposed methodology can successfully alleviate more than 53% of the peaks caused by the mass adoption of EVs with the better utilization of intermittent resources and substantial amount of profit