SCADA Office Building Implementation in the Context of an Aggregator

This paper at first presents an aggregation model including optimization tools for optimal resource scheduling and aggregating, and then, it proposes a real implemented SCADA system in an office building for decision support techniques and participating in demand response events. The aggregator model controls and manages the consumption and generation of customers by establishing contract with them. The SCADA based office building presented in this paper is considered as a customer of proposed aggregation model. In the case study, a distribution network with 21 buses, including 20 consumers and 26 distributed generations, is proposed for the aggregator network, and optimal resource scheduling of aggregator, and performance of implemented SCADA system for the office building, will be surveyed. The scientific contribution of this paper is to address from an optimization-based aggregator model to a SCADA based customer.This work has received funding from the Projects: NetEffiCity (ANI|P2020 18015); FEDER Funds through COMPETE program; National Funds through FCT under project UID/EEA/00760/2013; H2020 DREAM-GO Project (Marie Sklodowska-Curie grant agreement No 641794).info:eu-repo/semantics/publishedVersio

Modeling Local Energy Market for Energy Management of Multi-Microgrids

The diffusion of distributed energy resources (DERs) has changed the supply-demand balance of power systems. One option to modernize the management of the electricity distribution is to operate the distribution system with interconnected micro-grids (MGs). However, the MG participation in wholesale energy and ancillary service markets creates several challenges in the interactions among the energy market managing entities. To solve these problems, local energy markets (LEMs) have been proposed, where the MGs can trade energy with each other under the management of the LEM manager (LEMM) to minimize their operation cost. In this paper, a local energy market is modeled for multi-MGs (MMGs) to minimize the operation cost of MGs individually and their social welfare in cooperation with each other. In such model, the optimal scheduling of the DERs in each MG is done through the market clearing process. To investigate the effectiveness of the proposed approach, the local energy market is applied to a distribution network with three MGs

Flexibility Aggregation of Temporally Coupled Resources in Real Time Balancing Markets Using Machine Learning

In modern power systems with high penetration of renewable energy sources, the flexibility provided by distributed energy resources is becoming invaluable. Demand aggregators offer balancing energy in the real-time balancing market on behalf of flexible resources. A challenging task is the design of the offering strategy of an aggregator. In particular, it is difficult to capture the flexibility cost of a portfolio of flexibility assets within a price-quantity offer, since the costs and constraints of flexibility resources exhibit inter-temporal dependencies. In this article, we propose a generic method for constructing aggregated balancing energy offers that best represent the portfolio's actual flexibility costs, while accounting for uncertainty in future timeslots. For the case study presented, we use offline simulations to train and compare different machine learning (ML) algorithms that receive the information about the state of the flexible resources and calculate the aggregator's offer. Once trained, the ML algorithms can make fast decisions about the portfolio's balancing energy offer in the real-time balancing market. Our simulations show that the proposed method performs reliably towards capturing the flexibility of the Aggregator's portfolio and minimizing the aggregator's imbalances.</p

Approaching Prosumer Social Optimum via Energy Sharing with Proof of Convergence

With the advent of prosumers, the traditional centralized operation may become impracticable due to computational burden, privacy concerns, and conflicting interests. In this paper, an energy sharing mechanism is proposed to accommodate prosumers’ strategic decision-making on their self-production and demand in the presence of capacity constraints. Under this setting, prosumers play a generalized Nash game. We prove main properties of the game: an equilibrium exists and is partially unique; no prosumer is worse off by energy sharing and the price-of-anarchy is 1-O(1/I) where I is the number of prosumers. In particular, the PoA tends to 1 with a growing number of prosumers, meaning that the resulting total cost under the proposed energy sharing approaches social optimum. We prove that the corresponding prosumers’ strategies converge to the social optimal solution as well. Finally we propose a bidding process and prove that it converges to the energy sharing equilibrium under mild conditions. Illustrative examples are provided to validate the results

Coordination of specialised energy aggregators for balancing service provision

In the present context of evolution of the power and energy systems, more flexibility is required on the generation and demand side, to cope with the increasing uncertainty mostly introduced by variable renewable energy resources. This paper presents a conceptual framework that encompasses different types of aggregators, including local network aggregators, demand-side general aggregators, specialised energy aggregators (SEAs), and energy community aggregators. In this framework, this paper focuses on the coordination of SEAs to provide balancing services to the system operator. Each SEA manages a specific type of load, so that these loads can be managed by exploiting their control capabilities in a detailed way considering response time, dynamics and available flexibility. Moreover, the presence of the SEAs increases the privacy protection of the users, as only the information on a specific type of user's load is sent to the SEA. The SEA Coordinator interacts with the Balancing Service Provider aimed at procuring frequency containment, frequency restoration and replacement reserve services. This paper contains the SEA Coordinator formulation, information exchange and control operation strategies. Case study applications are presented by using SEAs for three specific types of loads (thermoelectric refrigerator, water booster pressure systems and electric vehicle charging stations). The results show how the control algorithm of the SEA Coordinator is effective in providing balancing services at different timings with the different types of loads. Various scenarios are considered, comparing an ideal situation without command propagation delays with realistic situations that take into account the command propagation delays

Bidding Strategy for Networked Microgrids in the Day-Ahead Electricity Market

In recent years, microgrids have drawn increasing attention from both academic and industrial sectors due to their enormous potential benefits to the power systems. Microgrids are essentially highly-customized small-scale power systems. Microgrids’ islanding capability enables microgrids to conduct more flexible and energy-efficient operations. Microgrids have proved to be able to provide reliable and environmental-friendly electricity to quality-sensitive or off-grid consumers. In addition, during the grid-connected operation mode, microgrids can also provide support to the utility grid. World-widely continuous microgrid deployments indicate a paradigm shift from traditional centralized large-scale systems toward more distributed and customized small-scale systems. However, microgrids can cause as many problems as it solves. More efforts are needed to address these problems caused by microgrids integration. Considering there will be multiple microgrids in future power systems, the coordination problems between individual microgrids remain to be solved. Aiming at facilitating the promotion of microgrids, this thesis investigates the system-level modeling methods for coordination between multiple microgrids in the context of participating in the market. Firstly, this thesis reviews the background and recent development of microgrid coordination models. Problems of existing studies are identified. Motivated by these problems, the research objectives and structure of this thesis are presented. Secondly, this thesis examines and compares the most common frameworks for optimization under uncertainty. An improved unit commitment model considering uncertain sub-hour wind power ramp behaviors is presented to illustrate the reformulation and solution method of optimization models with uncertainty. Next, the price-maker bidding strategy for collaborative networked microgrids is presented. Multiple microgrids are coordinated as a single dispatchable entity and participate in the market as a price-maker. The market-clearing process is modeled using system residual supply/demand price-quota curves. Multiple uncertainty sources in the bidding model are mitigated with a hybrid stochastic-robust optimization framework. What’s more, this thesis further considers the privacy concerns of individual microgrids in the coordination process. Therefore a privacy-preserving solution method based on Dantzig-Wolfe decomposition is proposed to solve the bidding problem. Both computational and economic performances of the proposed model are compared with the performances of conventional centralized coordination framework. Lastly, this thesis provides suggestions on future research directions of coordination problems among multiple microgrids

Optimal Demand Response Strategy in Electricity Markets through Bi-level Stochastic Short-Term Scheduling

Current technology in the smart monitoring including Internet of Things (IoT) enables the electricity network at both transmission and distribution levels to apply demand response (DR) programs in order to ensure the secure and economic operation of power systems. Liberalization and restructuring in the power systems industry also empowers demand-side management in an optimum way. The impacts of DR scheduling on the electricity market can be revealed through the concept of DR aggregators (DRAs), being the interface between supply side and demand side. Various markets such as day-ahead and real-time markets are studied for supply-side management and demand-side management from the Independent System Operator (ISO) viewpoint or Distribution System Operator (DSO) viewpoint. To achieve the research goals, single or bi-level optimization models can be developed. The behavior of weather-dependent renewable energy sources, such as wind and photovoltaic power generation as uncertainty sources, is modeled by the Monte-Carlo Simulation method to cope with their negative impact on the scheduling process. Moreover, two-stage stochastic programming is applied in order to minimize the operation cost. The results of this study demonstrate the importance of considering all effective players in the market, such as DRAs and customers, on the operation cost. Moreover, modeling the uncertainty helps network operators to reduce the expenses, enabling a resilient and reliable network.A tecnologia atual na monitorização inteligente, incluindo a Internet of Things (IoT), permite que a rede elétrica ao nível da transporte e distribuição faça uso de programas de demand response (DR) para garantir a operação segura e económica dos sistemas de energia. A liberalização e a reestruturação da indústria dos sistemas de energia elétrica também promovem a gestão do lado da procura de forma otimizada. Os impactes da implementação de DR no mercado elétrico podem ser expressos pelo conceito de agregadores de DR (DRAs), sendo a interface entre o lado da oferta e o lado da procura de energia elétrica. Vários mercados, como os mercados diário e em tempo real, são estudados visando a gestão otimizada do ponto de vista do Independent System Operator (ISO) ou do Distribution System Operator (DSO). Para atingir os objetivos propostos, modelos de otimização em um ou dois níveis podem ser desenvolvidos. O comportamento das fontes de energia renováveis dependentes do clima, como a produção de energia eólica e fotovoltaica que acarretam incerteza, é modelado pelo método de simulação de Monte Carlo. Ainda, two-stage stochastic programming é aplicada para minimizar o custo de operação. Os resultados deste estudo demonstram a importância de considerar todos os participantes efetivos no mercado, como DRAs e clientes finais, no custo de operação. Ainda, considerando a incerteza no modelo beneficia os operadores da rede na redução de custos, capacitando a resiliência e fiabilidade da rede

Operational planning and bidding for district heating systems with uncertain renewable energy production

In countries with an extended use of district heating (DH), the integrated operation of DH and power systems can increase the flexibility of the power system achieving a higher integration of renewable energy sources (RES). DH operators can not only provide flexibility to the power system by acting on the electricity market, but also profit from the situation to lower the overall system cost. However, the operational planning and bidding includes several uncertain components at the time of planning: electricity prices as well as heat and power production from RES. In this publication, we propose a planning method that supports DH operators by scheduling the production and creating bids for the day-ahead and balancing electricity markets. The method is based on stochastic programming and extends bidding strategies for virtual power plants to the DH application. The uncertain factors are considered explicitly through scenario generation. We apply our solution approach to a real case study in Denmark and perform an extensive analysis of the production and trading behaviour of the DH system. The analysis provides insights on how DH system can provide regulating power as well as the impact of uncertainties and renewable sources on the planning. Furthermore, the case study shows the benefit in terms of cost reductions from considering a portfolio of units and both markets to adapt to RES production and market states