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This book analyzes new electricity pricing models that consider uncertainties in the power market due to the changing behavior of market players and the implementation of renewable distributed generation and responsive loads. In-depth chapters examine the different types of market players including the generation, transmission, and distribution companies, virtual power plants, demand response aggregators, and energy hubs and microgrids. Expert authors propose optimal operational models for short-term performance and scheduling and present readers with solutions for pricing challenges in uncertain environments. This book is useful for engineers, researchers and students involved in integrating demand response programs into smart grids and for electricity market operation and planning.

Proposes optimal operation models;Discusses the various players in today's electricity markets;Describes the effects of demand response programs in smart grids.



Chapter 1. Energy Harvesting Technologies and Market Opportunities

Energy harvesting (EH) is a process in which ambient energies are utilized to form effective energies using various advanced techniques. Growing demand for energy in major end-use industries and green powered technologies are expected to drive the overall EH market. Indeed, the significant growth of the market can be attributed to the increasing installation of wireless sensor networks (WSNs) and Internet of Things (IoT) which are expected to boost the EH market through increasing self-powered sensors. In general, this chapter investigates the EH framework based on energy sources and technologies, intelligent solutions, and market opportunities.
Farzad H. Panahi, Fereidoun H. Panahi

Chapter 2. Electricity Market Pricing: Uniform Pricing vs. Pay-as-Bid Pricing

Selecting proper pricing rule is essential for the efficient operation of power markets. There are two main pricing rules in the existing power markets, namely uniform pricing (UP) and pay-as-bid (PAB) pricing. Among auction theorists, it is an unsolved question whether uniform pricing is superior to pay-as-bid pricing or the opposite. In this chapter, the aim is not to test various market benchmarks to answer the mentioned question; instead, the existing works relating introducing of and comparison between UP and PAB pricing schemes are reviewed, and the answer of the question is prescribed from each literature’s perspective.
Alireza Akbari-Dibavar, Behnam Mohammadi-Ivatloo, Kazem Zare

Chapter 3. Integrated Gas and Power Networks

This chapter proposes the integrated planning for the expansion and operation of the power system and the gas grid. In order to optimize energy usage and increase the efficiency, the simultaneous planning of the gas and electricity networks has been widely investigated. To this end, the use of devices and equipment connecting electricity and gas infrastructures such as Energy Hub and PtG have been considered, which have made the connection of these two infrastructures at different energy levels. The strong interdependence of these two infrastructures has encouraged researchers to consider security and economic issues simultaneously for these two infrastructures and to study them as an integrated system. There are various ways to plan expansion and operation that are discussed in detail in this chapter.
Alireza SoltaniNejad, Ramin Bahmani, Heidarali Shayanfar

Chapter 4. Transmission Pricing: Right Insights for Suitable Cost Allocation Methods

Power sector is undergoing an expressive energy transition process, which increases the uncertainties across the whole sector. Transmission cost allocation (TCA) methods must be improved to contemplate this new paradigm. In order to obtain an efficient pricing and to encourage the more optimized use of system global resources, modern TCA methods must capture the observed integration trend of devices and systems. The current TCA methods need to be improved and new approaches developed based on the right insights identified in this chapter. We may affirm that the better TCA methods are straight, elegant, and without arbitrary and subjective decisions.
M. A. Benetti, M. Sperandio

Chapter 5. Quantifying the Effect of Autonomous Demand Response Program on Self-Scheduling of Multi-carrier Residential Energy Hub

In this chapter, an overall model is proposed for energy management in the form of energy hub for residential sectors to reduce the operating costs and air pollution by utilization of different technologies such as renewables and demand response (DR) resources. In so doing, a residential building has been considered as an energy hub that receives natural gas and electricity at its inputs and delivers electricity and heat to consumers at its output. Also, the consumers include controllable and uncontrollable loads that controllable loads are controlled by the DR program. The main goal of this chapter is to lower the costs of operation and air pollution of the energy hub. The results show that when the price of natural gas is low, the energy hub uses natural gas to supply electric and thermal demands, and when the price of electricity is low, it uses electricity to feed consumers’ demand.
Amin Namvar, Farhad Samadi Gazijahani, Javad Salehi

Chapter 6. Offering Strategy of Thermal-Photovoltaic-Storage Based Generation Company in Day-Ahead Market

Designing appropriate strategies for the participation of generation companies (GenCos) in the electricity markets has always been a concern for researchers. Generally, a set of dispatchable and non-dispatchable units constitute GenCos. This chapter presents a coordinated offering structure for the participation of a GenCo consisting of thermal, photovoltaic (PV), and battery storage system (BSS) in the day-ahead (DA) electricity market. The proposed offering structure is formulated as a three-stage stochastic programming problem while a scenario-based technique is utilized to handle the uncertainty related to electricity prices and PV production. From another point of view, a compatible risk-measuring index with multi-stage stochastic programming problems, namely conditional value at risk (CVaR), is also considered in the proposed structure. The proposed offering model is not only able to derive the offering curves of GenCo but also is capable of applying various emission limitations pertaining to thermal units.
Hooman Khaloie, Amir Abdollahi, Sayyad Nojavan, Miadreza Shafie-Khah, Amjad Anvari-Moghaddam, Pierluigi Siano, João P. S. Catalão

Chapter 7. Risk-Based Purchasing Energy for Electricity Consumers by Retailer Using Information Gap Decision Theory Considering Demand Response Exchange

Electricity retailer using demand response (DR) programs can reduce their cost in procuring consumers energy. In this chapter, several new demand response schemes are proposed to reduce retailer cost. These new schemes include pool-order DR, forward DR, and reward-base DR. Information gap decision theory (IGDT) technique is proposed to handle the pool market price uncertainty. Furthermore, optimal bidding strategy of electricity retailer is obtained using IGDT technique based on opportunity and robustness functions. Optimal bidding strategy provides stepwise power price in the power price uncertainty condition for submiting to day-ahead market in order to purchase power from pool market. The proposed model based on IGDT technique can be solved using standard Branch and Bound (SBB) solver under GAMS software.
Ramin Nourollahi, Sayyad Nojavan, Kazem Zare

Chapter 8. Stochastic Cooperative Charging Scheduling of PEV Fleets in Networked Microgrids Considering Price Responsive Demand and Emission Constraints

This chapter deals with the problem of optimal power scheduling of electric vehicles in the microgrids to simultaneously optimize the operating expenditure and air pollution considering different types of plug-in electric vehicles with diverse travel patterns. To this end, a novel stochastic cost emission-based framework is suggested under practical constraints of electric vehicles. Besides, demand response program has been executed to smooth the consumption profile of customers aimed at decreasing the billing cost of system. The proposed problem is minimized by an integrated population-based metaheuristics algorithm namely grey wolf optimizer and Taguchi test method that can acquire a satisfactory solution. The results obtained from simulations declare a significant reduction in the operating cost of system and improve its technical features by employing electric vehicles. The proposed approach not only reduces the air emission, but also minimizes the operating costs by optimal utilization of electric vehicles.
Mehdi Shamshirband, Farhad Samadi Gazijahani, Javad Salehi

Chapter 9. Robust Scheduling of Plug-In Electric Vehicles Aggregator in Day-Ahead and Reserve Markets

In recent years, the role of electric vehicles in the transportation system is growing because of the unfavorable effects of the use of fossil fuels. Aggregation of plug-in electric vehicles can provide enough energy for them to act as demand-side resources and participate in electricity markets. The operation schedule of plug-in electric vehicles aggregators needs to be optimized in order to maximize their benefits. In this chapter, the optimal scheduling problem of plug-in electric vehicles aggregators for participation in day-ahead and reserve markets is studied. The uncertainty of market price is applied using robust optimization approach. The proposed model is a mixed-integer linear programming model. The model was implemented on a test system and solved using the General Algebraic Modeling System (GAMS) software. The results indicate that with a 2.51% decrease of aggregator’s total profit, the aggregator will be robust against 20% changes in the market price.
Amir Farahmand-Zahed, Sayyad Nojavan, Kazem Zare

Chapter 10. Optimal Scheduling of Water Distribution Systems’ Participation in Demand Response and Frequency Regulation Services

Water distribution systems (WDSs) are energy-intensive substructures that consume energy to deliver water to consumers. WDSs are able to provide demand response in power systems due to the existence of water storage tanks and variable speed pumps. Reducing operating costs and losses are the main outcomes of demand response programs which would lead to more economical operation of the system. In this chapter, a model for optimizing the participation of WDSs in the demand response market is presented, and the uncertainty of the energy price forecast is considered using Robust Optimization Approach. The objective of the optimization is to find the best schedule for operation of water tanks and pumps, in which the WDS’s water purchase cost is minimized and the WDS’s profit for providing the demand response services is maximized. The proposed model is implemented on a test WDS. The results indicate the effectiveness of the proposed model.
Amir Farahmand-Zahed, Alireza Akbari-Dibavar, Behnam Mohammadi-Ivatloo, Kazem Zare

Chapter 11. Optimal Power Scheduling of a GenCo Using Two-Point Estimate Method

Optimal scheduling of a generating company (GenCo) is necessary in the day-ahead electricity market to obtain maximum profit. But, the market price uncertainty may lead to negative effects for GenCo which should be modeled in the uncertain environment. First, a deterministic-based model via Mixed-Integer Quadratic Constrained Program (MIQCP) is formulated in this study to obtain optimal scheduling of GenCo. Then, a two-point estimate method (TPEM) is proposed to model the market price uncertainty in order to obtain uncertainty-based scheduling of GenCo. The proposed approach is investigated on two GenCo comprising 5-unit and 54-unit thermal generation to show the capabilities of the proposed approach in a large test system. Furthermore, the obtained results based on proposed approach are compared with the Monte Carlo Simulation (MCS) and deterministic approach in order to show the efficiency of the proposed approach in the uncertain environment.
Kittisak Jermsittiparsert

Chapter 12. Bidding and Offering Strategies for Integration of Battery Storage System and Wind Turbine

Utilization of wind turbine as renewable energies is increased due to environmental issues. In this work, a new structure is presented for integration of battery storage system (BSS) and wind turbine (WT) in the operation mode. In the proposed model, the BSS can be supplied and charged through WT or power procurement from the upstream grid. Stored power in the BSS can be sold to electricity market in high price in peak periods while by considering power market prices, the output power of WT can be directly injected to the electricity grid or can charge the BSS. A stochastic framework is proposed to consider uncertainties of wind speed and market price. Wind speed and market price scenarios are produced with Weibull and normal distribution functions, respectively. An MIP method is used to create the optimal offering and bidding curves for each hour in order to bid/offer for purchasing/selling power from/to upstream grid. Finally, obtained results are presented and discussed.
Kittisak Jermsittiparsert


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