Combined Heat and Power Economic Dispatch for Isolated Microgrids

Microgrids (MGs) have gathered significant attention over the last decade due to their potential to integrate Renewable Energies (RE) into power systems, in a reliable and efficient way, and their ability to provide sustainable energy supply solutions for remote areas without a connection to the main grid. Microgrids used for the latter application are known as isolated MGs since they are permanently operating in stand-alone mode. Isolated MGs have specific technical features such as low inertia and a critical demand-supply balance constraint, which hinder their operation, especially in cases with high penetration of intermittent and fluctuating RE. Moreover, it is expected that Combined Heat and Power Systems (CHP) will play an important role in MGs since these systems can considerably improve the overall system efficiency. CHP systems introduce additional technical challenges mainly related to the heat-power dependency of CHP and the thermal and power demand uncertainty. Thus, in order to guarantee a reliable and economic operation of isolated MGs integrating CHP units, it is important to design adequate strategies and methods for their different control levels. In these microgrids, the Energy Management System (EMS) has the main function of optimizing their operation through the solution of optimization problems such as Unit Commitment (UC), economic dispatch and/or optimal power flow. Hence, this thesis seeks to address the aforementioned challenges by proposing a novel energy management system (EMS) approaches for isolated MGs with CHP units and high penetration of RE. First, an EMS algorithm is proposed, based on an Affine Arithmetic-based Unit Commitment (AAUC) problem for day-ahead dispatch, using uncertainty intervals of both load and RE to provide robust commitment and dispatch solutions in AA form, which are feasible for all the possible realizations within the predetermined uncertainty bounds. A real-time dispatch solution is then found by the proposed algorithm, which computes the noise symbols values of the affine forms obtained by the AAUC, based on the current and actual load, the RE power levels and the available reserves. If the actual forecast error is outside the uncertainty bounds considered in the AAUC solution process, leading to possible load and/or RE curtailment, the AAUC is recalculated with updated forecast information. The proposed AA-based EMS is tested on a modified CIGRE microgrid benchmark and is compared against day-ahead deterministic, Model Predictive Control (MPC), stochastic optimization, and stochastic-MPC approaches. The simulation results show that the proposed EMS provides robust and adequate cost-effective solutions, without the need of frequent re-calculations as with MPC-based approaches, or assumptions regarding statistical characteristics of the uncertainties as in the case of stochastic optimization. Finally, a novel approach for the optimal economic dispatch of CHP MGs is proposed, which incorporates an Affine Arithmetic-based Economic Dispatch (AAED) problem into an MPC framework. The proposed algorithm solves each ∆t minutes (e.g. 15m) an AAED problem with time steps of ∆t minutes over a time horizon T (e.g. 24h). It uses the available forecast and the current state of the system, to provide the schedule and the affine forms that represent the operation intervals of the generators and Energy Storage Systems (ESS) for the next time interval [t, t + ∆t]. Online set points for generators and ESS are then obtained by computing the noise symbols values of the affine forms, based on the most updated information of electricity and heat demands and available renewable energy power. A theoretical CHP-based MG, comprising PVs, a gas boiler, a CHP unit, a battery, and a thermal tank, is used to assess the performance of the AA-MPC approach in both connected and isolated operation modes. The method is also compared with a deterministic MPC approach. Results show the ability of the method to better address forecasting errors, resulting in more cost-effective solutions, without considerably affecting the computation performance.Resumen: El concepto de microrredes ha ganado importancia en los ´ultimos a˜nos debido a que facilita la integraci´on de energ´ıas renovables a los sistemas de potencia de forma confiable y eficiente y a que provee soluciones sostenibles de suministro de energ´ıa para ´areas remotas sin conexi´on a la red el´ectrica principal. Las microrredes utilizadas en dichas ´areas remotas se conocen como microrredes aisladas, pues funcionan permanentemente en modo aut´onomo. Estas microrredes tienen caracter´ısticas t´ecnicas espec´ıficas tales como baja inercia y restricci´on cr´ıtica de balance entre generaci´on y demanda, que dificultan su adecuada operaci´on, especialmente en casos con alta penetraci´on de energ´ıas renovables de car´acter fluctuante e intermitente. Adicionalmente, se espera que los sistemas de cogeneraci´on (CHP por sus siglas en ingl´es) jueguen un papel importante en las microrredes, pues dichos sistemas pueden mejorar considerablemente la eficiencia global del sistema. Sin embargo, los CHP introducen desaf´ıos t´ecnicos adicionales relacionados principalmente con la relaci´on que existe entre la generaci´on de electricidad y la generaci´on de calor, as´ı como la incertidumbre asociada a la demanda t´ermica. Por lo tanto, para garantizar un funcionamiento confiable y econ´omico de las microrredes aisladas con CHP, es importante dise˜nar estrategias y m´etodos adecuados para sus diferentes niveles de control. En estas microrredes, el sistema de gesti´on de la energ´ıa (EMS por sus siglas en ingl´es) tiene la funci´on principal de optimizar la operaci´on a trav´es de la soluci´on de problemas de optimizaci´on tales como el problema de compromiso de unidades (UC por sus siglas en ingl´es), el despacho econ´omico y/o el flujo de potencia ´optimo. Por lo tanto, esta tesis busca abordar los desaf´ıos mencionados, proponiendo enfoques novedosos de EMS para microrredes aisladas con sistemas de cogeneraci´on y alta penetraci´on de renovables. En primer lugar, se propone un algoritmo de EMS que integra una formulaci´on del problema de UC en donde la incertidumbre asociada a la generaci´on con renovables y a la demanda es modelada por medio de la t´ecnica matem´atica conocida como “Affine Arithmetic (AA)”. Al resolver dicho problema, nombrado aqu´ı AAUC, se obtienen soluciones robustas de despacho en el dominio de la AA, las cuales son factibles para todos los posibles escenarios dentro de los l´ımites de incertidumbre considerados. Posteriormente, haciendo uso de la informaci´on m´as reciente de demanda, potencia generada con renovables y reservas disponibles y de las predicciones de dichas variables, se calculan los valores de los “noise symbols” de las “affine forms” obtenidas en el AAUC, obteniendo as´ı soluciones de despacho en tiempo real. Si el error de predicci´on est´a fuera de los l´ımites considerados en el proceso de soluci´on del AAUC, lo que conlleva a un deslastre de carga o de generaci´on con renovables, el AAUC se recalcula con un pron´ostico actualizado. El EMS propuesto es probado en un modelo de microrred propuesto por el CIGRE y se compara con otros m´etodos disponibles en la literatura, tales como el control predictivo por modelo (MPC por sus siglas en ingl´es), la optimizaci´on estoc´astica y el m´etodo combinado estoc´astico-MPC. Los resultados de la simulaci´on muestran que el EMS propuesto ofrece soluciones de operaci´on econ´omicas y confiables, sin la necesidad de efectuar c´alculos recurrentes con un costo computacional asociado o de hacer suposiciones con respecto a las caracter´ısticas estad´ısticas de las incertidumbres, como en el caso de los enfoques basados en MPC u optimizaci´on estoc´astica. Finalmente, se presenta un nuevo enfoque para el despacho econ´omico ´optimo de CHP microrredes, el cual incorpora dentro de un esquema de MPC un problema de despacho econ´omico basado en AA, denominado AAED. El algoritmo propuesto resuelve cada ∆t minutos (ej. 15 m) un problema AAED para un horizonte de tiempo T (ej. 24 h) dividido en pasos de tiempo de ∆t minutos. Utilizando el pron´ostico disponible y el estado actual del sistema, la soluci´on de dicho problema proporciona el compromiso de unidades y las “affine forms” que representan los intervalos de operaci´on de los generadores y de los sistemas de almacenamiento de energ´ıa para el siguiente intervalo de tiempo [t, t + ∆t]. Posteriormente, con base en la informaci´on m´as actualizada disponible de demanda de electricidad y de calor y de la energ´ıa generada con renovables, se calculan los valores de los “noise symbols” de las “affine forms” con el fin de obtener las referencias de potencia en tiempo real para los controles locales de los generadores y sistemas de almacenamiento. Para validar y comparar el m´etodo propuesto, se utiliza una microrred te´orica que comprende paneles fotovoltaicos, una caldera a gas, una unidad de CHP, una bater´ıa y un tanque t´ermico. El m´etodo se compara con el enfoque determinista de MPC en los dos modos de operaci´on de la microrred: conectada y aislada. Los resultados muestran la capacidad del m´etodo propuesta para enfrentar m´as apropiadamente los errores de pron´ostico, lo que resulta en soluciones de operaci´on m´as econ´omicas, sin afectar de forma considerable el rendimiento de computo.Doctorad

Distributed MPC for coordinated energy efficiency utilization in microgrid systems

To improve the renewable energy utilization of distributed microgrid systems, this paper presents an optimal distributed model predictive control strategy to coordinate energy management among microgrid systems. In particular, through information exchange among systems, each microgrid in the network, which includes renewable generation, storage systems, and some controllable loads, can maintain its own systemwide supply and demand balance. With our mechanism, the closed-loop stability of the distributed microgrid systems can be guaranteed. In addition, we provide evaluation criteria of renewable energy utilization to validate our proposed method. Simulations show that the supply demand balance in each microgrid is achieved while, at the same time, the system operation cost is reduced, which demonstrates the effectiveness and efficiency of our proposed policy.Accepted manuscrip

Assessing and comparing a DDPG model and GA optimization for a heat and power virtual power plant operating in a power purchase agreement scheme

This paper proposes a deep deterministic policy gradient (DDPG) model for the operation management of a solar power-based virtual power plant (VPP) having a PPA with the grid and supplying power and thermal energy to consumers. The VPP serves to balance the solar power intermittency, cover the demand whenever solar power is absent, and ensure an efficient supply of energy. The literature in this field has introduced optimization algorithms to determine the power plant’s output power or heat on a rolling-horizon basis. Using the function approximation category, which involves reinforcement learning with neural networks, to solve the simultaneous thermal and power operation management of VPPs is still not well developed. The challenges imposed in this model are sourced from the non-linearity of the CCHP, the power and thermal balance constraints, and the consideration of continuous variables rather than discrete ones. A case study is simulated in Egypt to assess and compare the models. Compared to the genetic algorithm optimization, the proposed DDPG model achieved 3% more profit, 12% higher carbon dioxide (CO2) emissions, and 9% lower natural gas consumption. The DDPG solution was 57% faster than the GA. The results of the DDPG model proved that machine learning methods could outperform optimization in terms of optimality achievement and speed of solution. The DDPG improved the operation of energy storage units and was able to recognize the supply-demand operational pattern, ensuring the scalability of the VPP to cope with different energy demand levels

Risk-Averse Decentralized Optimal Scheduling of a Virtual Energy Hub Plant Equipped with Multi Energy Conversion Facilities in Energy Markets

Distributed multi-energy systems, in addition to their advantages, pose significant challenges to future energy networks. One of these challenges is how these systems participate in energy markets. To overcome this issue, this paper introduces a virtual energy hub plant (VEHP) comprised of multiple energy hubs (EHs) to participate in the energy market in a cost-effective manner. Each EH is equipped with multiple distributed energy resources (DERs) in order to supply electrical, heating and cooling loads. Moreover, an integrated demand response (IDR) program and vehicle-to-grid (V2G) capable electric vehicles (EVs) are taken into consideration to enhance the flexibility to EHs. The manager of the VEHP participates in the existing day-ahead markets on behalf of EHs after collecting their bids. Since EHs are independent entities, a hybrid model of mobile edge computing system and analytical target cascading theory (MECATC) is proposed to preserve data privacy of EHs. Further, to tackle the uncertainty of renewables, a robust optimization method is applied. Obtained results corroborated the proposed scheduling is efficient and could increase the VEHP’s profit about 21.4% in light of using flexible technologies

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

Towards Optimal Management in Microgrids: An Overview

A microgrid is a set of decentralized loads and electricity sources, mainly renewable. It can operate connected to and synchronized with a traditional wide-area synchronous grid, i.e., a macrogrid, but can also be disconnected to operate in “island mode” or “isolated mode”. When this microgrid is able to manage its own resources and loads through the use of smart meters, smart appliances, control systems, and the like, it is referred to as a smart grid. Therefore, the management and the distribution of the energy inside the microgrid is an important issue, especially when operating in isolated mode. This work presents an overview of the different solutions that have been tested during the last few years to manage microgrids. The review shows the variety of mature and tested solutions for managing microgrids with different configurations and under several approaches