phd thesis on demand response

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A real-time demand response pricing model for the smart grid

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Big data analytics for demand response in smart grids

Oyedokun, James Timilehin (2021) Big data analytics for demand response in smart grids. PhD thesis, University of Glasgow.

The transition to an intelligent, reliable and efficient smart grid with a high penetration of renewable energy drives the need to maximise the utilisation of customers’ demand response (DR) potential. More so, the increasing popularity of smart meters deployed at customers’ sites provides a vital resource where data driven strategies can be adopted in enhancing the performance of DR programs. This thesis focuses on the development of new methods for enhancing DR in smart grids using big data analtyics techniques on customers smart meter data. One of the main challenges to the effective and efficient roll out of DR programs particularly for peak load reduction is identifying customers with DR potential. This question is answered in this thesis through the proposal of a shape based clustering algorithm along with novel features to target customers. In addition to targeting customers for DR programs, estimating customer demand baseline is one of the key challenges to DR especially for incentive-based DR. Customer baseline estimation is important in that it ensures a fair knowledge of a customers DR contribution and hence enable a fair allocation of benefits between the utility and customers. A Long Short-Term Memory Recurrent Neural Network machine learning technique is proposed for baseline estimation with results showing improved accuracy compared to traditional estimation methods. Given the effect of demand rebound during a DR event day, a novel method is further proposed for baseline estimation that takes into consideration the demand rebound effect. Results show in addition to customers baseline accurately estimated, the functionality of estimating the amount of demand clipped compared to shifted demand is added.

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Data-driven Demand Response Energy Management for Sustainable and Smart Manufacturing

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Electronic Theses and Dissertations

Optimization models and algorithms for demand response in smart grid..

Guangyang Xu , University of Louisville Follow

Date on Master's Thesis/Doctoral Dissertation

Document type.

Doctoral Dissertation

Degree Name

Industrial Engineering

Degree Program

Industrial Engineering, PhD

Committee Chair

Committee co-chair (if applicable).

Bae, Kihwan

Committee Member

Saleem, Jason

McIntyre, Michael

Author's Keywords

optimization; demand response; distributed models; load control; pricing schemes

For demand response in smart grid, a utility company wants to minimize total electricity cost and end users want to maximize their own utility. The latter is considered to consist of two parts in this research: electricity cost and convenience/comfort. We first develop a system optimal (SO) model and a user equilibrium (UE) model for the utility company and end users, respectively and compare the difference of the two. We consider users' possible preference on convenience over cost-saving under the real-time pricing in smart grid, and each user is assumed to have a preferred time window for using a particular appliance. As a result, each user in the proposed energy consumption game wishes to maximize a payoff or utility consisting of two parts: the negative of electricity cost and the convenience of using appliances during their preferred time windows. Numerical results show that users with less flexibility on their preferred usage times have larger impact on the system performance at equilibrium. Second, we found that instead of minimizing total cost, if utility company is regulated to maximize the social welfare, the user equilibrium model can achieve identical optimal solution as the system optimal model. We then design a demand response pricing frame work to accomplish this goal under alternative secondary objectives. We also investigate the non-uniqueness of the user equilibrium solution and prove that there exist alternative user equilibrium solutions. In this case, robust pricing is considered using multi-level optimization for the user equilibrium. Third, we study empirical data from a demand response pilot program in Kentucky in an attempt to understand consumer behavior under demand response and to characterize the thermo dynamics when set point for heat, ventilation and air conditioning (HVAC) is adjusted for demand response. Although sample size is limited, it helps to reveal the great variability in consumers' response to demand response event. Using the real data collected, we consider to minimize the peak demand for a system consisting of smart thermostats, advanced hot water heaters and battery systems for storage. We propose a mixed integer program model as well as a heuristic algorithm for an optimal consumption schedule so that the system peak during a designated period is minimized. Therefore, we propose a consumption scheduling model to optimally control these loads and storage in maximizing efficiency without impacting thermal comfort. The model allows pre-cooling and pre-heating of homes to be performed for variable loads in low-demand times. We propose several future works. First, we introduce the concept of elastic demand to our SO model and UE model. The system problem maximizes net benefit to the energy consumers and the user problem is the usual one of finding equilibrium with elastic demand. The Karush-Kuhn-Tucker (KKT) conditions can be applied to solve the elastic demand problems. We also propose to develop algorithms for multi-level pricing models and further collect and analyze more field data in order to better understand energy users' consumption behavior.

Recommended Citation

Xu, Guangyang, "Optimization models and algorithms for demand response in smart grid." (2016). Electronic Theses and Dissertations. Paper 2597. https://doi.org/10.18297/etd/2597

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PhD - AI-based demand-response strategies for an energy-consuming operator F/M

ref :2024-34839 | 22 Mar 2024

apply before : 30 Sep 2024

44 avenue de la republique 92320 Chatillon - France

about the role

Overall context and problem of the subject Your role is to carry out a PhD on AI-based "demand-response" strategies for a prosumer energy operator in the context of the energy and ecological transition: electrification, proliferation of renewable energies, storage optimisation and energy flexibility. The latter can be achieved in particular by implementing "demand-response" strategies, which involve aligning consumption with renewable energy production. These demand-response strategies can be implemented in a marketplace made up of consumers and producers/prosumers and governed by the law of supply and demand. Through its energy assets (antennas, batteries, solar panels, electric cars, buildings, etc.), the operator can take part in demand-response contracts. These strategies encompass a number of techniques, including the implementation of global or partial network standby modes and the sharing of infrastructure between operators, while maintaining a good level of service.

Scientific objective - results and challenges The aim of the PhD is to optimise demand-response strategies for a telecom operator in a context of uncertainty (variable production of renewable energy, fluctuations in prices and consumption, volume of network traffic) and faced with adversarial or cooperative agents participating in the exchange. The function to be optimised will take into account the carbon footprint, energy sale/purchase prices, quality of service, etc. And the decision-making policy will be based on storage management, electricity trading, putting base stations on standby, etc.

The main obstacles to be overcome : 1. Modelling of a multi-agent system capable of integrating demand-response strategies. 2. Multi-objective optimisation: reducing the carbon footprint, reducing electricity bills, preserving quality of service. 3. Alignment of time scales between decisions applied to the electricity network and the telecoms network. 4. Heterogeneous nature of actions (continuous, discrete). 5. Complexity of implementing deep reinforcement learning algorithms: convergence problem, safe exploration. 6. Robustness of the model in an environment different from that of the simulator.

Key deliverables: 1. Simulator of an exchange integrating radio sites as a reinforcement learning environment covering several scenarios. 2. Design and development of multi-agent deep reinforcement learning algorithms to optimise decision-making. 3. Design risk-free exploration strategies. 4. Study robustness with analytical and experimental results. 5. Scientific publications.

Skills and personal qualities required by the post

- In-depth knowledge of artificial intelligence, in particular reinforcement learning and deep learning,

- Skills in optimisation theory and game theory will be highly appreciated,

- Knowledge of mobile access networks is desirable,

- Fluency in Python programming, in particular PyTorch.

Education required 

Master's degree or engineering degree specialising in AI.

additional information

- Development in a stimulating environment within a department specialising in artificial intelligence and data science and staffed by experts in the field.

- Participating in Orange's involvement in CSR (Corporate Social Responsibility) issues, particularly the energy transition.

- Strengthening Orange's ambitions in the field of energy.

- Research work requiring an interdisciplinary approach, crossing several areas of computer science, mathematics and telecommunications such as data prediction, reinforcement learning, multi-agent systems, neural networks, mobile access network (MAN) architecture and modelling the energy consumption of 4G/5G base stations and beyond, etc...

- Potential contribution to collaborative projects.

The Innovation Division's ambition is to take Orange's innovation even further and strengthen its technological leadership, by mobilizing our research capabilities to nurture responsible innovation at the service of people, inform the Group's long-term strategic choices and influence the global digital ecosystem. We train the technology experts of today and tomorrow, and ensure continuous improvement in the performance of our services and our efficiency. The Innovation Division employs 6,000 people worldwide dedicated to research and innovation, including 740 researchers. Carrying a global vision with a wide range of profiles (researchers, engineers, designers, developers, data scientists, sociologists, graphic designers, marketers, cybersecurity experts, etc.), the men and women of Innovation listen to and serve countries, regions and business units to make Orange a trusted multiservice operator.

The Data & AI division's objectives are to define Orange's Data & AI standards and to develop use cases, products and services based on AI and data science. The reporting team is DREAMS (Data science, REsearch, Algorithms & ModelS). Its projects concern the application of AI to network and service issues, the development of voicebots, and research into sustainable territories and healthcare.

Only your skills matter

Regardless of your age, gender, origin, religion, sexual orientation, neuroatypia, disability or appearance, we encourage diversity within our teams because it is a strength for the collective and a vector of innovation. Orange Group is a disabled-friendly company: don't hesitate to tell us about your specific needs.

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Demand Response in Renewable Energy System

The use of Single Microgrid and Multi-Microgrid plays a key role in generation of electricity and serve to all the needs. This area being more interesting since it helps in real time, so that our guidance for all PhD works is available in this field.

Our technical team is available 24/7 for research assistance

Send your techinical enquiries directly to our technical team via mail - [email protected] or you can send it to support team via WhatsApp

   This is the blog which gives you a bunch of info in energy generation system that is popular in recent days. The energy system has multiple concepts, among many we discuss demand response in this blog. From this you will know about renewable energy system, demand response, its methods and finally the tool that helps to work out this research with proper outcome. Let us move into the blog now.

Know about Renewable Energy System

   Renewable Energy System is RES which is becoming the main source to generate electricity from the natural resources. The locality where the particular naturel resource availability is more will be in use to build this system. If more than one resource is present at same location, then the hybrid RES is the one which could be build.  

What is Demand Response in RES?

      A demand response is an electricity system that gives chance for the consumer to manage energy as per their needs. This system in short as DR gives different pricing for the energy that is affordable for the customers. The DR system extends over Multi-Microgrid (MMG) in which many number of MGS are in link with each other. In general each MG will compose a storage battery as well as RES to produce energy and store it. As per the stored electricity and the demand of load this DR operates.    

What are the Methods in use on Demand Response RES?

• Multi-Objective Optimization Algorithm
• Deep Reinforcement Learning
• Artificial Intelligence
• Cooperative and Non-Cooperative Game Theory
• Mixed integer linear programming

What tool can you prefer for this DR?

      When it comes to implementation, without any doubt you can go with Matlab Simulink tool. This tool is the one that enable to create real RES in simulation using unique blocks for wind turbine and solar. In this you can make changes in the constant values as solar irradiation, wind, angle and so on, with respect to the energy source. By making changes you can extract results and validate the performance of your work that adds a credit to your research.

     Apart from the blocks in this tool, it also gives support to include algorithm to operate the values automatically and report the outcome in graphs. The variation in power is the main things in this DR and also it can handle pricing too.

   In this way, the nook and corners of this field is familiar to our team so we will be helpful in completing your PhD and master’s without any delay. Put a step forward into our contact and then we will communication with you and get in touch with you. We work at affordable price, even you can get to know about the payments alone. If you need any help, then you make a move and connect with us through online.

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