2,463 research outputs found
An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes
In the last decades, new types of generation technologies have emerged and have been gradually integrated into the existing power systems, moving their classical architectures to distributed systems. Despite the positive features associated to this paradigm, new problems arise such as coordination and uncertainty. In this framework, microgrids constitute an effective solution to deal with the coordination and operation of these distributed energy resources. This paper proposes a Genetic Algorithm (GA) to address the combined problem of Unit Commitment (UC) and Economic Dispatch (ED). With this end, a model of a microgrid is introduced together with all the control variables and physical constraints. To optimally operate the microgrid, three operation modes are introduced. The first two attend to optimize economical and environmental factors, while the last operation mode considers the errors induced by the uncertainties in the demand forecasting. Therefore, it achieves a robust design that guarantees the power supply for different confidence levels. Finally, the algorithm was applied to an example scenario to illustrate its performance. The achieved simulation results demonstrate the validity of the proposed approach.Ministerio de Ciencia, Innovación y Universidades TEC2016-80242-PMinisterio de Economía y Competitividad PCIN-2015-043Universidad de Sevilla Programa propio de I+D+
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Robust optimization for energy transactions in multi-microgrids under uncertainty
Independent operation of single microgrids (MGs) faces problems such as low self-consumption of local renewable energy, high operation cost and frequent power exchange with the grid. Interconnecting multiple MGs as a multi-microgrid (MMG) is an effective way to improve operational and economic performance. However, ensuring the optimal collaborative operation of a MMG is a challenging problem, especially under disturbances of intermittent renewable energy. In this paper, the economic and collaborative operation of MMGs is formulated as a unit commitment problem to describe the discrete characteristics of energy transaction combinations among MGs. A two-stage adaptive robust optimization based collaborative operation approach for a residential MMG is constructed to derive the scheduling scheme which minimizes the MMG operating cost under the worst realization of uncertain PV output. Transformed by its KKT optimality conditions, the reformulated model is efficiently solved by a column-and-constraint generation (C&CG) method. Case studies verify the effectiveness of the proposed model and evaluate the benefits of energy transactions in MMGs. The results show that the developed MMG operation approach is able to minimize the daily MMG operating cost while mitigating the disturbances of uncertainty in renewable energy sources. Compared to the non-interactive model, the proposed model can not only reduce the MMG operating cost but also mitigate the frequent energy interaction between the MMG and the grid
Control and Communication Protocols that Enable Smart Building Microgrids
Recent communication, computation, and technology advances coupled with
climate change concerns have transformed the near future prospects of
electricity transmission, and, more notably, distribution systems and
microgrids. Distributed resources (wind and solar generation, combined heat and
power) and flexible loads (storage, computing, EV, HVAC) make it imperative to
increase investment and improve operational efficiency. Commercial and
residential buildings, being the largest energy consumption group among
flexible loads in microgrids, have the largest potential and flexibility to
provide demand side management. Recent advances in networked systems and the
anticipated breakthroughs of the Internet of Things will enable significant
advances in demand response capabilities of intelligent load network of
power-consuming devices such as HVAC components, water heaters, and buildings.
In this paper, a new operating framework, called packetized direct load control
(PDLC), is proposed based on the notion of quantization of energy demand. This
control protocol is built on top of two communication protocols that carry
either complete or binary information regarding the operation status of the
appliances. We discuss the optimal demand side operation for both protocols and
analytically derive the performance differences between the protocols. We
propose an optimal reservation strategy for traditional and renewable energy
for the PDLC in both day-ahead and real time markets. In the end we discuss the
fundamental trade-off between achieving controllability and endowing
flexibility
Review of trends and targets of complex systems for power system optimization
Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107
Power Management of Remote Microgrids Considering Battery Lifetime
Currently, 20% (1.3 billion) of the world’s population still lacks access to electricity and many live in remote areas where connection to the grid is not economical or practical. Remote microgrids could be the solution to the problem because they are designed to provide power for small communities within clearly defined electrical boundaries. Reducing the cost of electricity for remote microgrids can help to increase access to electricity for populations in remote areas and developing countries. The integration of renewable energy and batteries in diesel based microgrids has shown to be effective in reducing fuel consumption. However, the operational cost remains high due to the low lifetime of batteries, which are heavily used to improve the system\u27s efficiency. In microgrid operation, a battery can act as a source to augment the generator or a load to ensure full load operation. In addition, a battery increases the utilization of PV by storing extra energy. However, the battery has a limited energy throughput. Therefore, it is required to provide a balance between fuel consumption and battery lifetime throughput in order to lower the cost of operation. This work presents a two-layer power management system for remote microgrids. The first layer is day ahead scheduling, where power set points of dispatchable resources were calculated. The second layer is real-time dispatch, where schedule set points from the first layer are accepted and resources are dispatched accordingly. A novel scheduling algorithm is proposed for a dispatch layer, which considers the battery lifetime in optimization and is expected to reduce the operational cost of the microgrid. This method is based on a goal programming approach which has the fuel and the battery wear cost as two objectives to achieve. The effectiveness of this method was evaluated through a simulation study of a PV-diesel hybrid microgrid using deterministic and stochastic approach of optimization
Risk-Averse Model Predictive Operation Control of Islanded Microgrids
In this paper we present a risk-averse model predictive control (MPC) scheme
for the operation of islanded microgrids with very high share of renewable
energy sources. The proposed scheme mitigates the effect of errors in the
determination of the probability distribution of renewable infeed and load.
This allows to use less complex and less accurate forecasting methods and to
formulate low-dimensional scenario-based optimisation problems which are
suitable for control applications. Additionally, the designer may trade
performance for safety by interpolating between the conventional stochastic and
worst-case MPC formulations. The presented risk-averse MPC problem is
formulated as a mixed-integer quadratically-constrained quadratic problem and
its favourable characteristics are demonstrated in a case study. This includes
a sensitivity analysis that illustrates the robustness to load and renewable
power prediction errors
A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization
In this paper, a novel model predictive control strategy, with a 24-h prediction horizon, is
proposed to reduce the operational cost of microgrids. To overcome the complexity of the optimization
problems arising from the operation of the microgrid at each step, an adaptive evolutionary strategy
with a satisfactory trade-off between exploration and exploitation capabilities was added to the
model predictive control. The proposed strategy was evaluated using a representative microgrid that
includes a wind turbine, a photovoltaic plant, a microturbine, a diesel engine, and an energy storage
system. The achieved results demonstrate the validity of the proposed approach, outperforming
a global scheduling planner-based on a genetic algorithm by 14.2% in terms of operational cost.
In addition, the proposed approach also better manages the use of the energy storage system.Ministerio de Economía y Competitividad DPI2016-75294-C2-2-RUnión Europea (Programa Horizonte 2020) 76409
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