scholarly journals Exploring the Potentialities of Deep Reinforcement Learning for Incentive-Based Demand Response in a Cluster of Small Commercial Buildings

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2933
Author(s):  
Davide Deltetto ◽  
Davide Coraci ◽  
Giuseppe Pinto ◽  
Marco Savino Piscitelli ◽  
Alfonso Capozzoli
Keyword(s):  
Reinforcement Learning ◽  
Demand Response ◽  
Control Strategy ◽  
Energy Demand ◽  
Hybrid Control ◽  
Renewable Energy Sources ◽  
Economic Benefits ◽  
Commercial Buildings ◽  
Electricity Sector ◽  
Hybrid Controller

Demand Response (DR) programs represent an effective way to optimally manage building energy demand while increasing Renewable Energy Sources (RES) integration and grid reliability, helping the decarbonization of the electricity sector. To fully exploit such opportunities, buildings are required to become sources of energy flexibility, adapting their energy demand to meet specific grid requirements. However, in most cases, the energy flexibility of a single building is typically too small to be exploited in the flexibility market, highlighting the necessity to perform analysis at a multiple-building scale. This study explores the economic benefits associated with the implementation of a Reinforcement Learning (RL) control strategy for the participation in an incentive-based demand response program of a cluster of commercial buildings. To this purpose, optimized Rule-Based Control (RBC) strategies are compared with a RL controller. Moreover, a hybrid control strategy exploiting both RBC and RL is proposed. Results show that the RL algorithm outperforms the RBC in reducing the total energy cost, but it is less effective in fulfilling DR requirements. The hybrid controller achieves a reduction in energy consumption and energy costs by respectively 7% and 4% compared to a manually optimized RBC, while fulfilling DR constraints during incentive-based events.

scholarly journals Management and Activation of Energy Flexibility at Building and Market Level: A Residential Case Study

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1188 ◽  
Author(s):  
Paolo Taddeo ◽  
Alba Colet ◽  
Rafael E. Carrillo ◽  
Lluc Casals Canals ◽  
Baptiste Schubnel ◽  
...  
Keyword(s):  
Demand Response ◽  
Energy Demand ◽  
Renewable Energy Sources ◽  
Test Site ◽  
Electricity Sector ◽  
Energy Sources ◽  
Multi Level ◽  
The Difference ◽  
Real Test

The electricity sector foresees a significant change in the way energy is generated and distributed in the coming years. With the increasing penetration of renewable energy sources, smart algorithms can determine the difference about how and when energy is produced or consumed by residential districts. However, managing and implementing energy demand response, in particular energy flexibility activations, in real case studies still presents issues to be solved. This study, within the framework of the European project “SABINA H2020”, addresses the development of a multi-level optimization algorithm that has been tested in a semi-virtual real-time configuration. Results from a two-day test show the potential of building’s flexibility and highlight its complexity. Results show how the first level algorithm goal to reduce the energy injected to the grid is accomplished as well as the energy consumption shift from nighttime to daytime hours. As conclusion, the study demonstrates the feasibility of such kind of configurations and puts the basis for real test site implementation.

scholarly journals Research on Economic Operation Strategy of CHP Microgrid Considering Renewable Energy Sources and Integrated Energy Demand Response

2019 ◽  
Vol 11 (18) ◽  
pp. 4825 ◽  
Author(s):  
Jun Dong ◽  
Shilin Nie ◽  
Hui Huang ◽  
Peiwen Yang ◽  
Anyuan Fu ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Demand Response ◽  
Energy Demand ◽  
Renewable Energy Sources ◽  
Rapid Development ◽  
Utilization Efficiency ◽  
Energy Output ◽  
Mixed Integer ◽  
Energy Structure ◽  
Economic Operation

Renewable energy resources (RESs) play an important role in the upgrading and transformation of the global energy structure. However, the question of how to improve the utilization efficiency of RESs and reduce greenhouse gas emissions is still a challenge. Combined heating and power (CHP) is one effective solution and has experienced rapid development. Nevertheless, with the large scale of RESs penetrating into the power system, CHP microgrid economic operation faces great challenges. This paper proposes a CHP microgrid system that contains renewable energy with considering economy, the environment, and system flexibility, and the ultimate goal is to minimize system operation cost and carbon dioxide emissions (CO2) cost. Due to the volatility of renewable energy output, the fuzzy C-means (FCM) and clustering comprehensive quality (CCQ) models were first introduced to generate clustering scenarios of the renewable energy output and evaluate the clustering results. In addition, for the sake of improving the flexibility and reliability of the CHP microgrid, this paper considers the battery and integrated energy demand response (IEDR). Moreover, the strategy choices of microgrid operators under the condition of grid-connected and islanded based on environment and interest aspects are also developed, which have rarely been involved in previous studies. Finally, this stochastic optimization problem is transformed into a mixed integer linear programming (MILP), which simplifies the calculation process, and the results show that the operation mode under different conditions will have a great impact on microgrid economic and environmental benefits.

scholarly journals An Innovative Stochastic Multi-Agent-Based Energy Management Approach for Microgrids Considering Uncertainties

Inventions ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 37 ◽  
Author(s):  
Sajad Ghorbani ◽  
Rainer Unland ◽  
Hassan Shokouhandeh ◽  
Ryszard Kowalczyk
Keyword(s):  
Energy Management ◽  
Demand Response ◽  
Energy Demand ◽  
Search Algorithm ◽  
Renewable Energy Sources ◽  
Management Approach ◽  
Agent Based ◽  
Multi Agent ◽  
Power Outputs ◽  
Microgrid Energy Management

In microgrids a major share of the energy production comes from renewable energy sources such as photovoltaic panels or wind turbines. The intermittent nature of these types of producers along with the fluctuation in energy demand can destabilize the grid if not dealt with properly. This paper presents a multi-agent-based energy management approach for a non-isolated microgrid with solar and wind units and in the presence of demand response, considering uncertainty in generation and load. More specifically, a modified version of the lightning search algorithm, along with the weighted objective function of the current microgrid cost, based on different scenarios for the energy management of the microgrid, is proposed. The probability density functions of the solar and wind power outputs, as well as the demand of the households, have been used to determine the amount of uncertainty and to plan various scenarios. We also used a particle swarm optimization algorithm for the microgrid energy management and compared the optimization results obtained from the two algorithms. The simulation results show that uncertainty in the microgrid normally has a significant effect on the outcomes, and failure to consider it would lead to inaccurate management methods. Moreover, the results confirm the excellent performance of the proposed approach.

scholarly journals Bifurcation and Hybrid Control for A Simple Hopfield Neural Networks with Delays

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Zisen Mao ◽  
Hao Wang ◽  
Dandan Xu ◽  
Zhoujin Cui
Keyword(s):  
Neural Network ◽  
Hopf Bifurcation ◽  
Control Strategy ◽  
State Feedback ◽  
Hybrid Control ◽  
Hopfield Neural Network ◽  
Parameter Perturbation ◽  
Hybrid Controller ◽  
Simulation Results ◽  
Delayed State Feedback

A detailed analysis on the Hopf bifurcation of a delayed Hopfield neural network is given. Moreover, a new hybrid control strategy is proposed, in which time-delayed state feedback and parameter perturbation are used to control the Hopf bifurcation of the model. Numerical simulation results confirm that the new hybrid controller using time delay is efficient in controlling Hopf bifurcation.

scholarly journals Biomass Production in Agroforestry Systems: V.E.Ri.For Project

2014 ◽  
Vol 11 ◽  
pp. 58-63 ◽  
Author(s):  
Andrea R. Proto ◽  
Giuseppe Zimbalatti ◽  
Lorenzo Abenavoli ◽  
Bruno Bernardi ◽  
Soraya Benalia
Keyword(s):  
Energy Demand ◽  
Fossil Fuels ◽  
Energy Use ◽  
Renewable Energy Sources ◽  
Local System ◽  
Agroforestry Systems ◽  
Economic Benefits ◽  
Alternative Source ◽  
Woody Perennials ◽  
Marginal Areas

The biomass for energy purposes, coming from agroforestry systems and timber industry, can provide various environmental and socio-economic benefits. Among all renewable energy sources, agroforestry biomass represents both an important alternative source to fossil fuels and an opportunity for the socio-economic development of various marginal areas in Italy. In particular, agroforestry is a collective name of land use systems in which woody perennials are grown in association with herbaceous plants and/or livestock in a spatial arrangements, a rotation, or both in which there are both ecological and economic interactions between the tree and the non-tree components of the system. Estimating availability of biomass resources is important to assess bioenergy production potential and so bioenergy contribution to annual energy demand. In the supply of biomass to energy use, the planning of operations is the basis for sustainable development of agroforest system. Most existing forest practice rules and recommendations did not anticipate this increased extraction of woody biomass and offer no specific guidance on how much removal is healthy for ecosystems. Intensification of biomass utilization, particularly for energy and fuel needs, presents a range of potential environmental risks. Therefore, the research focuses on development of guidelines for increasing a sustainable biomass supply chain at local scale, in order to facilitate energy planning that considers the local system carrying capacity and the potential of substitution of fossil fuels.

scholarly journals MPC-Based Arctan Droop Control Strategy of the Parallel Inverter System in an Islanded AC Microgrid

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
J. D. D. Iyakaremye ◽  
G. N. Nyakoe ◽  
C. W. Wekesa
Keyword(s):  
Control Strategy ◽  
Hybrid Control ◽  
Electronic Device ◽  
Renewable Energy Sources ◽  
Droop Control ◽  
Local Loads ◽  
Distributed Generations ◽  
Voltage Frequency ◽  
Islanded Microgrid ◽  
Virtual Impedance

Distributed generations (DG) are one of the upcoming technologies recently used by many electric utilities in all corners of the world. Most of those DG form the microgrid (MG) to serve local loads and can be connected to the grid. This DG’s technology is enabled by utilizing renewable energy sources (REs) that are ecofriendly; however, these REs are intermittent by their nature, so controlling a power electronic device interfaced with them to be connected to the grid is another challenge. Many researchers have worked on the inverters’ control in MG. This study also elaborates on the control strategy for inverters adapted to REs for proper control of voltage and frequency used in an islanded microgrid. The study proposes a hybrid control strategy made of the virtual impedance droop control with arctan function and model predictive control. Extensive simulations have been carried out to validate the proposed control strategy’s effectiveness in terms of rapid transient response and stabilization of voltage, frequency, and power equitability among the microsources in the islanded microgrid.

scholarly journals Influence of energy demand response actions on thermal comfort and energy cost in electrically heated residential houses

2016 ◽  
Vol 26 (3) ◽  
pp. 298-316 ◽  
Author(s):  
Behrang Alimohammadisagvand ◽  
Sadaf Alam ◽  
Mubbashir Ali ◽  
Merkebu Degefa ◽  
Juha Jokisalo ◽  
...  
Keyword(s):  
Thermal Comfort ◽  
Indoor Air ◽  
Demand Response ◽  
Control Strategy ◽  
Energy Cost ◽  
Energy Demand ◽  
Cold Climate ◽  
Indoor Climate ◽  
Electricity Prices ◽  
Operative Temperatures

This study has two aims to investigate the energy demand response (DR) actions on thermal comfort and energy cost in detached residential houses (1960, 2010 and passive) in a cold climate. The first one is to find out the acceptable range of indoor air and operative temperatures complying with the recommended thermal comfort categories in accordance with the EN 15251 standard. The second one is to minimize the energy cost of electric heating system by means of the DR control strategy, without sacrificing thermal comfort of the occupants. This research was carried out with the validated dynamic building simulation tool IDA Indoor Climate and Energy. Three different control strategies were studied: A) a strategy based on real-time hourly electricity price, B) new DR control strategy based on previous hourly electricity prices and C) new predictive DR control strategy based on future hourly electricity prices. The results show that the lowest acceptable indoor air and operative temperatures can be reduced to 19.4℃ and 19.6℃, respectively. The maximum annual saving in total energy cost is about 10% by using the control algorithm C.

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