Sharif Digital Repository

Real-time electricity prices along with demand-side potentials can provide distribution companies (DisCos) with considerable financial and technical benefits compared to the conventional flat prices. This paper incorporates demand response in DisCos' short-term decision model in a real-time pricing (RTP) environment wherein consumers are charged based on hourly varying prices. Besides the hourly RTP sale prices, the established model deals with other DisCo's short-term activities including hourly purchases from the grid, commitment of distributed generation (DG) units, dispatch of shunt compensators, and invocation of load curtailments (LCs). The stochastic nature of wholesale market prices... 

Cooperative cloud providers in the form of cloud federations can potentially reduce their energy costs by exploiting electricity price fluctuations across different locations. In this environment, on the one hand, the electricity price has a significant influence on the federations formed, and, thus, on the profit earned by the cloud providers, and on the other hand, the cloud cooperation has an inevitable impact on the performance of the smart grid. In this regard, the interaction between independent cloud providers and the smart grid is modeled as a two-stage Stackelberg game interleaved with a coalitional game in this article. In this game, in the first stage the smart grid, as a leader... 

Machine learning (ML) is generating new opportunities for innovative research in energy economics and finance. We critically review the burgeoning literature dedicated to Energy Economics/Finance applications of ML. Our review identifies applications in areas such as predicting energy prices (e.g. crude oil, natural gas, and power), demand forecasting, risk management, trading strategies, data processing, and analyzing macro/energy trends. We critically review the content (methods and findings) of more than 130 articles published between 2005 and 2018. Our analysis suggests that Support Vector Machine (SVM), Artificial Neural Network (ANN), and Genetic Algorithms (GAs) are among the most... 

Different reasons such as power system restructuring, electricity price variation in some hours of a day and growth in the fuel price, have led to more attention to demand response (DR) programmes for consumers. Two important issues in DR programmes are emergency demand response programming (EDRP) and Interruptible/Curtail able (I/C) programme that is utilised as interruptible load contracts (ILC). The EDDR is utilised to decrease the consumption in peak load or critical hours of the day. This occurs by means of customer's behaviour in response to the incentives. However, the curtailment of voluntarily loads in the critical hours considering the consumer requirements is an action that can be... 

Residential energy management (REM) program is a demand response (DR) tool that automatically manages energy consumption of controllable household appliances to improve the energy consumption profile of a house according to electricity price. REM intends not only to improve technical aspects of distribution systems but also motivate customers for active participation in DR programs. In this regard, this paper proposes a two-level REM framework. In the first level, each customer runs an optimization problem to minimize his payment cost and sends the desired operation scheduling of appliances and the payment cost to the system operator. In the second level, a multiobjective (MO) optimization... 

In this paper, hydrogen management in refineries is examined considering a new perspective to optimize refinery costs. In order to minimize the total annual cost in refineries a mixed-integer non-linear programming (MINLP) model has been developed. Retrofitting the hydrogen network and determining the hydrogen surplus capacity of refineries are the main outputs of the proposed model. Then, to utilize this surplus capacity and increase the profitability of refineries, surplus hydrogen is used to produce electricity and ammonia. The results of a case study showed that retrofitting of the hydrogen network can save 12% in the consumption of fresh hydrogen from hydrogen plants. After retrofitting... 

The concept of energy hub has been used recently to study multi-carrier energy systems. A model has been proposed in this paper for planning multi-hub energy system considering the competition between the hubs. The energy hubs are interconnected by a power grid. Hubs supply the demands for heat and electricity using various technologies and access to several energy carriers. To this aim, load zones were used to incorporate demand profiles for both heat and electricity in the different seasons of the year. Supplying the demand most cost-effectively is the objective function of each hub. These hubs select the optimum strategy separately in a competitive space. The power grid is owned by... 

Growing interest in the intermittent renewable energy sources may jeopardize the flexibility of power systems. In order to improve the flexibility of modern power systems, the surplus electricity generated by renewable sources can be deployed into several carriers, such as natural gas and heating energy via power-to-gas (PtG) and power-to-heat (PtH) technologies. This paper proposes an optimal daily energy management model of residential energy hubs integrated with power-to-X technologies. The proposed energy hub is incorporated with PtG, PtH, combined heat and power (CHP) facilities, and thermal storage to meet the required electrical, gas, and heating demands. In order to capture the... 

A massive focus has recently been made on demand response (DR) programs, aimed to the electricity price reduction, reliability improvement, and energy efficiency. Basically, DR programs are divided into twofold main categories, namely incentive-based programs and price- or time-based programs. The focus of this paper is on priced-based DR programs including consumer responses to the time differentiated pricing. Home load management (HLM) program is designed to control responsive appliances and charging/discharging cycles of plug-in hybrid electric vehicles (PHEVs) by the consumer. Uncertain parameters associated with PHEV, i.e. its departure/travelling time and energy consumption as well as... 

New technologies such as smart homes and appliances and renewable energy production have been attracting much attention in recent years. In practice, the management of these technologies to get the minimum household payment has become a challenge due to the time-varying electricity price. In this paper, we propose a real time energy management system to manage the appliances and storages. The solar energy is charged in the storages and used together with the grid electricity to supply the appliances. The proposed management system uses an ILP-based Home Energy Management (ILPHEM) optimization engine. ILPHEM schedules appliances requests and storage usage based on updated real time input data... 

A massive focus has recently been made on demand response (DR) programs, aimed to the electricity price reduction, reliability improvement, and energy efficiency. Basically, DR programs are divided into twofold main categories, namely incentive-based programs and price- or time-based programs. The focus of this paper is on priced-based DR programs including consumer responses to the time differentiated pricing. Home load management (HLM) program is designed to control responsive appliances and charging/discharging cycles of plug-in hybrid electric vehicles (PHEVs) by the consumer. Uncertain parameters associated with PHEV, i.e. its departure/travelling time and energy consumption as well as... 

This paper investigates a reliability-centered approach for the placement procedure of remote terminal units (RTUs) in power distribution systems. Entire attempt is contributed to incorporate the practical aspects interrelated with the problem, e.g. the accessibility to the candidate substations, the load point criticality, as well as the load probabilistic behavior and consequently the inevitable variation in the electricity price. The placement process is done through an optimization problem wherein the common reliability indices, as the decisive factors in the placement process, are going to be minimized. The approach is successfully applied to a real power distribution feeder of Iran's... 

In deregulated power systems, distribution network operators (DNO) are responsible for maintaining the proper operation and efficiency of distribution networks. This is achieved traditionally through specific investments in network components. The event of distributed generation (DG) has introduced new challenges to these distribution networks. The role of DG units must be correctly assessed to optimise the overall operating and investment cost for the whole system. However, the distributed generation owners (DGOs) have different objective functions which might be contrary to the objectives of DNO. This study presents a long-term dynamic multi-objective model for planning of distribution... 

The growing worldwide demand for less polluting shapes of energy have led to a renewed interest in the use of Micro Combined Heat and Power (Micro CHP) technologies in the residential sector. Micro CHP have been introduced around Iran recently, and expected to diffuse more and more.In this paper, technical and economic studies for the use of Micro CHP in the different climate zones of Iran are executed. These zones are categorized in to five; Tehran, Rasht, Bandar Abbas, Ardebil and Yazd, based on weather conditions. Later on using an economic model, both annual energy savings and percentage of system profitability in each zone are calculated as well as reduction in annual emissions. It... 

Unit Commitment is the optimization process for minimizing the total cost of real power generation meeting the generating units and the system constraints. With the restructuring in power systems, the matter of great concern is the fuel cost and consequently electricity price increase in some hours of the day. Accordingly, the optimum performance of Demand Side Management (DSM) has been gaining great attention recently. In this paper, the effect of the optimum performance of Emergency Demand Response Program (EDRP) on the cost of the unit commitment problem considering the line flow constrains is scrutinized. Due to the fact that the simultaneous adoption of the unit commitment and demand... 

In future smart grids, energy storage systems (EESs) and demand response (DR) are expected to have an important role in balancing load changes arising from life-style of consumers and varying output of some renewable energy resources. However, activating DR and installing ESSs need huge investment forcing system owner to justify their effectiveness prior to any implementation. This paper presents a model to find the optimal capacity of EESs in distribution systems with demand responding to electricity price changes. The model aims at maximizing system owner profit by lessening total electricity provision costs. The model is formulated as a mixed integer non-linear programming problem. Due to... 

With recent deregulation in electricity industry, price forecasting has become the basis for this competitive market. The precision of this forecasting is essential in bidding strategies. So far, the artificial neural networks which can find an accurate relation between the historical data and the price have been used for this purpose. One major problem is that, they usually need a large number of training data and neurons either for complex function approximation and data fitting or classification and pattern recognition. As a result, the network topology has a significant impact on the network computational time and ability to learn and also to generate unseen data from training data. To... 

This paper finds near equilibrium prices for electricity markets with non-convexities due to binary variables, in order to reduce the market participants’ opportunity costs, such as generators’ unrecovered costs. The opportunity cost is defined as the difference between the profit when the instructions of the market operator are followed and when the market participants can freely make their own decisions based on the market prices. We use the minimum complementarity approximation to the minimum total opportunity cost model, from previous research, with tests on a much more realistic unit commitment model than in previous research, including features such as reserve requirements, ramping... 

The Iranian government has set a target of a 20% share of non-fossil fuel electricity generation by 2030, whose main result is reducing Green House Gas (GHG) emissions (about 182 million tonnes in 2017) to achieve the targets pledged under the Paris Climate Accord. So, this paper presents a comprehensive model on the expansion of non-fossil technology to evaluate the impact of increasing their share in Iran’s electricity supply system. This analytical approach is based on system dynamics (SD) that was developed based on dynamic behavior of electricity market, with an emphasis on the expansion of non-fossil fuels (solar photovoltaics, wind turbines, expansion turbines, and hydro power) in the...