Energy Management in Supply and Demand Sides

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Motivations and contributions of the thesis

Our motivations are two fold. First, to address the challenges detailed above, we believe that concepts from different disciplines should come together. In the beginning of this thesis, our area of expertise includes Computer Science, Network Communication and Telecommunication. From these fields, we rely on important concepts such as Network Calculus (NC) and queuing theory to address the challenges in energy domain which broaden our field of expertise. Second, nowadays researches on renewables and energy cost reduction are hot topics as a means of securing our energy future. Hence, we want to contribute our part to this global cause.
Contributions of this thesis are classified according to supply and demand sides of industrial microgrid energy management. They are listed below:
• Supply side:
– To attain the minimum power generation of DERs, we proposed a model based on service curves of NC. Based on actual power consumption data of a factory, we also proposed different strategies for minimizing energy costs.
– To mitigate power fluctuations using ESSs, a Gaussian-based smoothing algorithm is proposed. The algorithm attains lower ESSs size when compared to other smoothing algorithms.

Microgrid Architecture

An architecture of a microgrid is shown in Figure 2.1. The main microgrid components include solar PV panels, wind turbines, ESSs, loads (industrial loads in our case), energy spot markets, and a microgrid controller. Through a Point of Common Coupling (PCC) circuit breaker, it is possible to connect or disconnect the microgrid from the utility grid. Under normal conditions, the microgrid is connected to the utility grid for the purpose of energy transactions. However, when there is fault (e.g., power outage, low power quality, etc) in the utility grid, the PCC disconnects the microgrid to be an autonomous system, i.e., it is in island mode. In this case, local generations in the microgrid should support the loads of the microgrid.

Industrial loads and manufacturing types

Industrial load consists of electrical load demands by manufacturing plants or industries. According to Zhang et al. [Zha+16], most manufacturing plants have already installed smart meters and control infrastructures which are necessary for DR. In this section, we discuss a specific manufacturing type that we use for our work on DR in chapter 4. Furthermore, we provide a brief description of other manufacturing types (just for comparison of their working principles) as depicted in Figure 2.4.

Demand Response (DR)

In legacy power systems, the main focus has been improving power supply based on evolution of electricity demands. However, due to recent emergence of smart grids, a Demand Side Management (DSM) also plays a crucial role by managing flexible loads. According to a World Bank report prepared by River [Riv05], DSM is defined as follows.

Table of contents :

I General Introduction and Concepts
1 General Introduction
1.1 Introduction
1.2 Why microgrids?
1.3 Challenges in two sides of a microgrid
1.3.1 Supply side challenges
1.3.2 Demand side challenges
1.4 Motivations and contributions of the thesis
1.5 Thesis organization
2 General Concepts and Models
2.1 Introduction
2.2 Microgrid Concept
2.2.1 Microgrid Architecture
2.2.2 Models of Wind, Solar and Storage
2.2.2.1 Wind power
2.2.2.2 Solar PV power
2.2.2.3 Energy storage systems
2.2.3 Industrial loads and manufacturing types
2.2.3.1 Serial production (or transfer) lines
2.2.3.2 Assembly/disassembly lines
2.2.3.3 Parallel lines
2.2.3.4 Split/merge system
2.2.3.5 Closed-loop lines
2.2.4 Spot market
2.3 Demand Response (DR)
2.3.1 DR programs
2.3.1.1 Price-based DR programs
2.3.1.2 Incentive-based DR programs
2.3.2 Potential benefits of DR
2.3.3 Mathematical problems and approaches in DR
2.3.3.1 Utility maximization
2.3.3.2 Cost minimization
2.3.3.3 Price forecast
2.3.3.4 Renewable energy integration
2.3.4 OpenADR – a DR tool
2.3.4.1 OpenADR architecture
2.3.4.2 OpenADR services
2.3.4.3 OpenADR implementations
2.4 Service curves of Network Calculus
2.4.1 Min-plus and Max-plus Algebras
2.4.1.1 Min-plus Algebra
2.4.1.2 Max-plus Algebra
2.4.2 Service curve concepts
2.4.2.1 Common functions as service curves
2.4.2.2 Strict service curve
2.4.2.3 Maximum service curve
2.4.2.4 Concatenation and aggregation of service curves
2.4.3 Applications of Network Calculus
2.5 Queuing theory overview
2.5.1 Kendall’s notation for queues
2.5.2 Little’s Formula
2.5.3 Average-based performance measures of D/D/1 queue
2.5.3.1 Server Utilization
2.5.3.2 Mean queue length
2.5.3.3 Mean waiting time
2.5.4 Temporal evolution of arrivals and departures
2.5.5 Applications of queuing theory to manufacturing
2.6 Summary
II Energy Management in Supply and Demand Sides
3 Modeling and Smoothing of DERs
3.1 Introduction
3.2 Related work
3.2.1 DER modeling
3.2.2 Smoothing of energy production
3.3 DER modeling using Service Curves
3.3.1 Service curves of DERs
3.3.1.1 Service curves of solar and wind
3.3.1.2 Energy storage
3.3.2 Energy supply and demand balance
3.3.3 Cost minimization strategies
3.3.3.1 Strategy 1 – Sell excess energy
3.3.3.2 Strategy 2 – Store excess energy
3.3.3.3 Strategy 3 – Use external energy to charge battery
3.4 Smoothing renewable energy production
3.4.1 Smoothing algorithms
3.4.1.1 Moving average-based smoothing
3.4.1.2 Gaussian-based smoothing
3.4.2 Measure of smoothness
3.4.3 Constraint on successive power levels
3.4.4 Determining battery size
3.4.4.1 Charging capacity
3.4.4.2 Discharging capacity
3.4.4.3 Final battery capacity
3.5 Simulation results
3.5.1 Description of datasets
3.5.1.1 Solar PV data
3.5.1.2 Wind speed data
3.5.1.3 Battery parameters
3.5.1.4 Energy demand data
3.5.1.5 Spot market price data
3.5.2 Results and discussions on modeling of DERs
3.5.2.1 Performance of the strategies
3.5.2.2 Effect of battery sizes
3.5.2.3 Effect of spot market prices
3.5.2.4 Payback period estimation
3.5.3 Results and discussions on smoothing
3.5.3.1 Performance of the smoothing algorithms
3.5.3.2 The final power production curve
3.6 Summary
4 Industrial Demand Side Management
4.1 Introduction
4.2 Related Work
4.2.1 Production line modeling
4.2.2 Scheduling in production lines
4.3 Modeling a Synchronous Production Line
4.3.1 Virtual cell
4.3.2 Queuing theory-based model for the SPL
4.3.2.1 Arrival instants
4.3.2.2 Arrival processes at machines
4.3.2.3 Departure processes at machines
4.3.2.4 Utilization function
4.3.2.5 Power consumption and utilization function
4.4 Scheduling in the SPL system
4.4.1 Scheduling problem
4.4.2 DFSM schedule coding
4.4.3 Optimal schedule under DR
4.4.3.1 Phase 1 – Existence of feasible schedule
4.4.3.2 Phase 2 – Finding optimal schedule
4.4.3.3 Phase 3 – Path to optimal schedule
4.4.4 DR acceptance conditions
4.5 Analytical and Simulation Results
4.5.1 Results on SPL modeling
4.5.1.1 Analytical results
4.5.1.2 Simulation results
4.5.2 Result on DR scheduling
4.5.2.1 Finding schedule words
4.5.2.2 Monetary gains of accepting DR
4.6 Experimentation with OpenADR
4.6.1 Testbed setup
4.6.1.1 Hardware
4.6.1.2 Software
4.6.2 Description of a DR event
4.6.3 Communicating DR events between VTN and VEN
4.7 Summary
III Conclusions and Perspectives
5 Conclusions and Perspectives
5.1 Conclusions
5.1.1 Modeling of DERs and cost minimization strategies
5.1.2 Smoothing of RESs
5.1.3 A queue theory-based model of a production line
5.1.4 DR scheduling in a production line
5.2 Perspectives
List of symbols
Glossaries
Acronyms
Glossary
Author’s publication list
Bibliography