Estimated cost of electricity with time horizon for micro grids based on the policy response of demand for real price of energy

The intelligent microgrids are an efficient alternative, which allows to supply the demand decreasing the losses of the electrical system and at the same time; the environment and the consumers are the main beneficiaries. This article develops a heuristic based on an energy management model based on the real price of electricity, which will allow end users to encourage the implementation of a policy of response to demand, in order to optimize their consumption, for which a micro smart grid is analyzed, with conventional and non-conventional renewable generation, In addition, a mechanism of "real energy price" will be implemented as a policy of response to demand, with the aim of optimizing the costs of energy that will be transferred to users depending on the stratum to which it belongs, these costs will have a short-term horizon with hourly intervals, achieving a reduction in the purchase of energy from the syste

“Factibilidad para el diseño de una micro-red basado en la economía circular en el proceso de extracción de petróleo”

Dentro del artículo se determinó la factibilidad de implementación de un diseño basado en microredes y economía de combustible en el proceso de extracción de petróleo. La problemática principal se enfoca en la emisión de gases que tienen un impacto negativo en el medioambiente y son generados por las empresas petroleras. La economía circular juega un papel muy importante en este diseño, la integración de paneles fotovoltaicos y generadores diésel permitieron la reducción de costos operativos y la adecuada utilización de recursos energéticos. Bajo el escenario de una planta petrolera con operación de 24 horas el sistema propuesto en este estudio se considera factible dado que el algoritmo de optimización demuestra la reducción anual del consumo de diésel en un 56% y un decrecimiento respecto a los costos de operación con un 16%, de esta forma se disminuye la huella de emisión de gases contaminantes en el sector objeto de estudio.This article determined the feasibility of implementing a design based on micro-networking and fuel economy in the oil extraction process. The main problem is focused on the emission of gases that have a negative impact on the environment and are generated by oil companies. The circular economy plays a very important role in this design, the integration of photovoltaic panels and diesel generators allowed the reduction of operational costs and the adequate use of energy resources. Under the scenario of an oil plant with 24-hour operation, the system proposed in this study is considered feasible since the optimization algorithm shows an annual reduction of diesel consumption by 56% and a decrease in operating costs by 16%, thus reducing the footprint of pollutant gas emissions in the sector under study

Gestión energética para una óptima respuesta a la demanda en micro redes inteligentes.

In this document, we develop a model of energy management, to encourage users and customers to optimize demand, considering that, customers are connected by smart microchip with all the benefit it carries. "The price of real energy" is the mechanism used to give and respond to demand, which aims to transfer to customers the actual cost of electric power. It is necessary through Markov chains, it would have the possible demands in the next moment (in the next hour in general), and according to the demands, makes an economic delivery of the Distributed Generation plants, obtaining the real energetic cost. As a result of the management model, obtaining unic and different dynamic rates for each customer, with ranges of energy and power with each energy price. This rate is updated every hour, according to the historical demand of each client. The Management Model looks for customers to modify their customs amounts, with the objective of reducing the total price to be paid, helping to flatten the load of the curve and reducing the total cost of the electrical system.En el presente documento se desarrolla un modelo de gestión energético para incentivar en los usuarios/clientes una respuesta a la demanda óptima, considerando que, los clientes están conectados a una micro red inteligente, con todos los beneficios que esto conlleva. El mecanismo de respuesta a la demanda utilizada en el “precio real de la energía”, el cual tiene como objetivo, transferir a los clientes el costo real del servicio de energía eléctrica. Para esto, mediante cadenas de Markov, se estima las posibles demandas que tendrá el sistema en el siguiente instante (generalmente, en la siguiente hora) y, de acuerdo a estas demandas, se realiza un despacho económico de las centrales de Generación Distribuida, obteniendo el costo real de la energía. Como resultado del modelo de gestión se obtienen pliegos tarifarios dinámicos, únicos y diferentes para cada cliente, con rangos de consumo en energía y potencia, asumiendo su respectivo precio de la energía. Este pliego tarifario se actualiza horariamente de acuerdo a los datos históricos de demanda de cada cliente. En este sentido, lo que busca el modelo de gestión es que los clientes modifiquen sus estilos de consumo, con la finalidad de reducir el precio total a pagar, ayudando al aplanamiento de la curva de carga y a la reducción de los costos totales del sistema eléctrico

Energy management in microgrids with renewable energy sources: A literature review

Renewable energy sources have emerged as an alternative to meet the growing demand for energy, mitigate climate change, and contribute to sustainable development. The integration of these systems is carried out in a distributed manner via microgrid systems; this provides a set of technological solutions that allows information exchange between the consumers and the distributed generation centers, which implies that they need to be managed optimally. Energy management in microgrids is defined as an information and control system that provides the necessary functionality, which ensures that both the generation and distribution systems supply energy at minimal operational costs. This paper presents a literature review of energy management in microgrid systems using renewable energies, along with a comparative analysis of the different optimization objectives, constraints, solution approaches, and simulation tools applied to both the interconnected and isolated microgrids. To manage the intermittent nature of renewable energy, energy storage technology is considered to be an attractive option due to increased technological maturity, energy density, and capability of providing grid services such as frequency response. Finally, future directions on predictive modeling mainly for energy storage systems are also proposed

太陽光発電が導入された配電系統における蓄電池の最適計画・制御手法に関する研究

早大学位記番号:新8188早稲田大

Probabilistic dispatch of remote hybrid microgrids including battery storage and load management

This work presents a probabilistic economic dispatch tool for en-ergy management (EM) studies in the context of remote hybrid AC/DC microgrids (MGs). An EM approach is proposed to en-sure a reliable power supply at the minimum cost of the hybrid MG operation. A comprehensive operational framework is pre-sented, which considers topological features of the hybrid MG and the interlinking converter between AC and DC subsections. Approach and models are tested using several operating scenari-os referred to a test hybrid MG system. In the analyses, the opportunity of integrating battery energy storage and energy demand management in the EM scheme is investigated. The results of the analyses demonstrate the effectiveness and practicality of the optimization tool in different operation contexts

Energy Management of Grid-Connected Microgrids, Incorporating Battery Energy Storage and CHP Systems Using Mixed Integer Linear Programming

In this thesis, an energy management system (EMS) is proposed for use with battery energy storage systems (BESS) in solar photovoltaic-based (PV-BESS) grid-connected microgrids and combined heat and power (CHP) applications. As a result, the battery's charge/discharge power is optimised so that the overall cost of energy consumed is minimised, considering the variation in grid tariff, renewable power generation and load demand. The system is modelled as an economic load dispatch optimisation problem over a 24-hour time horizon and solved using mixed integer linear programming (MILP) for the grid-connected Microgrid and the CHP application. However, this formulation requires information about the predicted renewable energy power generation and load demand over the next 24 hours. Therefore, a long short-term memory (LSTM) neural network is proposed to achieve this. The receding horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the energy management system (EMS) that benefits from using actual generation and demand data in real-time. At each time-step, the LSTM predicts the generation and load data for the next 24 h. The dispatch problem is then solved, and the real-time battery charging or discharging command for only the first hour is applied. Real data are then used to update the LSTM input, and the process is repeated. Simulation results using the Ushant Island as a case study show that the proposed online optimisation strategy outperforms the offline optimisation strategy (with no RH), reducing the operating cost by 6.12%. The analyses of the impact of different times of use (TOU) and standard tariff in the energy management of grid-connected microgrids as it relates to the charge/discharge cycle of the BESS and the optimal operating cost of the Microgrid using the LSTM-MILP-RH approach is evaluated. Four tariffs UK tariff schemes are considered: (1) Residential TOU tariff (RTOU), (2) Economy seven tariff (E7T), (3) Economy ten tariff (E10T), and (4) Standard tariff (STD). It was found that the RTOU tariff scheme gives the lowest operating cost, followed by the E10T tariff scheme with savings of 63.5% and 55.5%, respectively, compared to the grid-only operation. However, the RTOU and E10 tariff scheme is mainly used for residential applications with the duck curve load demand structure. For community grid-connected microgrid applications except for residential-only communities, the E7T and STD, with 54.2% and 39.9%, respectively, are the most likely options offered by energy suppliers. The use of combined heat and power (CHP) systems has recently increased due to their high combined efficiency and low emissions. Using CHP systems in behind-the-meter applications, however, can introduce some challenges. Firstly, the CHP system must operate in load-following mode to prevent power export to the grid. Secondly, if the load drops below a predefined threshold, the engine will operate at a lower temperature and hence lower efficiency, as the fuel is only half-burnt, creating significant emissions. The aforementioned issues may be solved by combining CHP with a battery energy storage system. However, the dispatch of CHP and BESS must be optimised. Offline optimisation methods based on load prediction will not prevent power export to the grid due to prediction errors. Therefore, a real-time EMS using a combination of LSTM neural networks, MILP, and RH control strategy is proposed. Simulation results show that the proposed method can prevent power export to the grid and reduce the operational cost by 8.75% compared to the offline method. The finding shows that the BESS is a valuable asset for sustainable energy transition. However, they must be operated safely to guarantee operational cost reduction and longer life for the BESS

Optimal Operation and Maximal Hosting Capacity of High-Renewable Islanded Microgrids

With the advancement of technology, renewable power generators such as solar photovoltaics and wind turbines have become cost-effective and competitive compared to traditional generators. On the other hand, carbon emission issues have been globally focused, promoting development of renewable energy. Meanwhile, microgrids have been widely constructed with increasing installation of distributed generators including microturbines and renewable power generators. Challenges from intermittent and uncertain renewable sources, low operating efficiency as well as system stability in the islanded mode still exist for microgrid operation and renewable hosting capacity assessment. To address these unsolved issues, it is worth developing advanced optimal operation and hosting capacity maximization approaches for high-renewable microgrids, which are presented in this thesis. For microgrid operation, economic efficiency, solution robustness and system stability are major concerns to be addressed. In order to achieve cost-effective operation, firstly a new stochastic optimal power flow (OPF) is proposed for islanded microgrids. A linear network operating model which can be used in the OPF problem is specifically developed, while uncertainties of photovoltaic power and loads are addressed by Monte Carlo simulation. Secondly, an improved OPF method with a new iterative solution algorithm is proposed to enhance the accuracy of network operating model and the computing speed. Besides, an advanced probabilistic modelling method is adapted to present real-time uncertainties in the OPF method. Thirdly, a novel stochastic OPF method with consideration of tie-line switching from the grid-connected to the islanded mode while the main grid in contingency is proposed. Security constraints to guarantee the system stability in the islanded mode are formulated. Moreover, a Benders decomposition based solution algorithm is developed, to efficiently solve the OPF problem with a master problem and a sub-problem which formulate the grid-connected and the islanded modes, respectively. Fourthly, a renewable hosting capacity maximization approach for an islanded microgrid, considering system frequency deviation, is proposed. An advanced sensitivity region based optimization method is proposed to address the uncertainties of wind power and loads, thus obtaining a robust solution. The proposed methods have been successfully demonstrated and compared with existing works. Simulation results have verified their feasibility and effectiveness