MAnAGER - Models and algorithms for automated stand-alone and aggregated energy management systems to enhancing demand response in the SME and residential sectors
Title: MAnAGER - Models and algorithms for automated stand-alone and aggregated energy management systems to enhancing demand response in the SME and residential sectors
Coordinators at INESCC: Carlos Henggeler
Co-Coordinator: Marta Lopes
Project Team:
| Álvaro Gomes | DEEC-UC, INESC Coimbra | agomes@deec.uc.pt |
| António Gomes Martins | DEEC-UC, INESC Coimbra | agmartins@uc.pt |
| Carlos Henggeler Antunes | DEEC-UC, INESC Coimbra | ch@deec.uc.pt |
| Hermano Bernardo | INESC Coimbra | hermano.ber@gmail.com |
| Humberto Jorge | DEEC-UC, INESC Coimbra | hjorge@deec.uc.pt |
| Inês Reis | INESC Coimbra | inesfreis@deec.uc.pt |
| Ivo Gonçalves | INESC Coimbra | icpg@dei.uc.pt |
| João Trovão | Université de Sherbrooke, INESC Coimbra | joao.trovao@usherbrooke.ca |
| José Sousa | IPS, INESC Coimbra | jose.luis.sousa@estsetubal.ips.pt |
| Luís Dias | FEUC-UC, CEBER, INESC Coimbra | lmcdias@fe.uc.pt |
| Luís Neves | IPL, INESC Coimbra | luis.neves@ipleiria.pt |
| Maria João Alves | FEUC-UC, CEBER, INESC Coimbra | mjalves@fe.uc.pt |
| Marta Lopes | ESAC-IPC, INESC Coimbra | mlopes@esac.pt |
| Paulo Pereirinha | ISEC-IPC, INESC Coimbra | ppereiri@isec.pt |
Date of approval: 26-04-2018
Dates start/end: 09-07-2018 / 08-07-2021
Total Eligible Cost: 231 795.83€
EU Financial Support: 197 026.46€
National Financial Support: 34 769.37€
Project Code: POCI-01-0145-FEDER-028040
Region of intervention:
Beneficiaries: INESC Coimbra - Instituto de Engenharia de Sistemas e Computadores de Coimbra
Site: http://www.uc.pt/en/org/inescc/Projects/projects/MAnAGER
Synopsis
Grid management has undergone a paradigm shift from a demand-driven supply strategy to a model based on renewables generation, which introduces new supply and demand balancing challenges due to its intermittent nature. In this new scenario, energy resources management on the demand side in a residential and small and medium-sized enterprise (SME) context, using the flexibility that energy end-users provide regarding load operation, in combination with dynamic tariffs, behavioral change and the transformation of consumers into prosumers, offers a vast potential to be exploited for the integrated optimization of energy resources.
Goals
This project aims to develop models and algorithms for automated, stand-alone and aggregated energy management systems (EMS) to be integrated in smart grid technologies. This development will contribute to the increased penetration of renewable energy generation, increase electricity system sustainability and reliability, enhance the operation of energy markets by facilitating the participation of new players, and bring technical and economic benefits to residential consumers and SMEs.
Characterized by an interdisciplinary nature, this project includes the know-how and expertise from electrical engineering, decision analysis, operational research, behavioral analysis and economics and its main objectives are:
- To design and implement models and algorithms for automated stand-alone EMS for the integrated optimization of energy resources. To include multiple objectives into the model to account for the trade-offs between economic and comfort dimensions, considering the requirements and preferences of distinct types of consumers. To extend this work to aggregator entities operating as mediators between consumers and system operators and the energy market, which can offer bids for ancillary services using the flexibility negotiated with clusters of consumers endowed with standalone EMS.
- To develop innovative pricing mechanisms for parties’ negotiation in hierarchical decisions, e.g. retailers vs. consumers engaged in demand response through EMS as well as aggregators of demand flexibility vs. system operators/energy markets, based on bi-level mathematical programming (Stackelberg games).
- To characterize and exploit the issues associated with behavioral and organizational demand response in the SME and residential sectors, including the design of pre-specified profiles enabling to anticipate and facilitate the computation of optimal solutions for the integrated management of energy resources (also accounting for usability, control, privacy and feedback).
Tasks
The project involves four core activities:
- Optimization algorithms to enhance stand-alone and aggregated demand response flexibility. Leader: Carlos Henggeler Antunes
- Development of innovative tariff mechanisms for the retail electricity market. Leader: António Martins
- Behavioral and organizational demand response. Leader: Marta Lopes
- Integrated planning of the energy system to take full advantage of demand response. Leader: Luis Neves
Tasks and Milestones Schedule
More Detail About Milestones
| Milestone | Description |
| M1.1: Optimisation algorithms for stand-alone energy management systems. | Optimisation algorithms running incorporating multiple objectives and different inputs. Design and test of adaptive operators to increase computational performance and allow implementation in near real time. |
| M1.2: Optimisation algorithms for aggregated energy management systems. | Optimisation algorithms for an aggregator entity to operate as an intermediary between individual energy management systems and the system operator / energy market. |
| M2.1: Bi-level algorithms to optimise electricity dynamic pricing in retail markets. | Design, implementation and test of bi-level optimisation algorithms based on the hybridisation of evolutionary programming with exact algorithms to study the interactions between retailers and consumers endowed with energy management systems. |
| M2.2: Bi-level algorithms to optimise interactions between the grid/energy market, aggregators, and consumers. | Design, implementation and test of bi-level optimisation algorithms to optimise the interactions between the grid/energy market, aggregators, and consumers, including multiple objectives. |
| M3.1: End-users’ specifications of energy management systems. | Usability, control and feedback specifications of energy management systems assessed through international standards. |
| M3.2: Demand response flexibility simulation models for the SME and residential sectors. | Demand response flexibility simulation models that are specifically designed for the SME and residential sectors incorporating its behavioural and organizational specifications, to be used by electricity retailers. |
| M3.3: On-line demand response flexibility simulator open to the public. | On-line demand response flexibility simulator open to the public to facilitate the energy and economic impact assessment of different tariff structures thus supporting end-users’ decision making, in particular SME and residential end-users. |
| M4.1: Portfolio optimisation models. | Development of portfolio optimisation models to determine efficient cost-risk frontiers. Development of adequate algorithms based on mathematical programming and meta-heuristics to deal with those models. |
| M4.2: Bi-level models to reflect the impacts of demand response on the grid operation and planning. | Development of bi-level models to reflect the impacts of demand response on the grid operation and planning, extending the previous work by the research team on the computation of “extreme” solutions (regarding the leader/follower interactions). |
Expected Outputs
The expected results can be used by consumers, load serving entities and aggregators who may use the consumption patterns flexibility to provide system services. These developments will contribute to the increased penetration of renewable energy generation, increase the sustainability and reliability of the system, improve the operation of energy markets by facilitating the participation of new players and bring technical and economic benefits to residential consumers and SMEs.
