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The construction of electricity retail market poses unprecedented opportunities and challenges for the sale of electricity. The traditional mode of electricity business marketing is not suitable for the new market environment, and it needs change in many aspects. How to serve customers well in the competitive environment, seize the market, and improve their own operating efficiency is an urgent problem for power grid enterprises. This paper regards the development of electricity retail market as the diffusion process of innovation. Based on the diffusion of innovations theory, this paper analyzes the characteristics of different development stages, and formulates service strategies according to their characteristics, so as to provide reference for power grid enterprises to seize the market.

In this paper, the potential of reducing the electricity cost with the help of energy storage and controllable loads on the Russian retail electricity market is studied. The retail electricity market in Russia features different price categories, allowing consumers to reduce expenses with proper planning. Approximately 2/3 of the cost in flexible price categories is for the power consumption in specific hours, with the remaining 1/3 for the energy consumption itself. Because of the continued cost decrease of energy storage devices, they have become an increasingly more attractive investment, opening opportunities for economic savings combined with load shifting, which can occur either in a standalone manner or with the use of energy storage. In this paper, demand shifting is implemented in the Russian retail market, combined with the payback calculation for energy storage.

With deregulation of electrical market in the China, the electricity retail companies (ERCs) and the electricity retail market (ERM) experiences fast development in the recent years. Different from the generation and transmission market, the retail market is more open to the participators, and has more competition and higher risk. Considering the risk factors e.g. the business scale, the capital, the employee, and the complaints, an optimization model to reduce the risk of the ERCs and ERM with higher predicted risk is newly proposed in this paper, where the risk factors are normalized by the threshold value and the time before quantifying the risk. The risk optimization problem with constraints to adjustable risk factors is nonlinear and solved by the interior point method. The numerical results validate the optimization effect on the risk of the ERCs and the ERM.

This paper forecasts the risk of the electricity retail market (ERM). The risk factors of electricity retail companies (ERCs) includes the market share, capital, employee qualifications are considered. The capital factor includes the circulating capital, the ratio between the asset and liability, the credit deposit, and the net tangible asset. The employees' qualification considers the numbers and the ratio of the proficient employees. The risk factors are normalized to derive the comparable risk indices. The market shares are applied as weights of the risk factors. The risk of the ERC is quantified at two levels. The Gaussian process regression (GPR) was applied to fit the historical risk curve, and forecast the future risk of the ERCs and the ERM. The risk indices and the forecast results of a retail market with 9 retail companies are provided to show the feasibility and accuracy of the proposed model.

With the onset of dementia primarily, but not exclusively, on elders and the lack of pharmaceutical treatment, lifestyle monitoring through technology seems to be a prominent solution. The human effort, error and cost imposed by close monitoring can be largely mitigated by promising eHealth technological solutions. Wearable, wristband or wristwatch, trackers are flooding the retail market at affordable prices. However, their acceptability and effectiveness for clinical purposes and especially the growing elderly population and their caregivers are still being investigated. This explorative study aims to answer such questions after carefully designing a questionnaire tailored to 45 end-users distributed in three groups of 15 participants each: healthcare professionals (HCP), caregivers and people with Mild Cognitive impairment (MCI) related to AD. Findings include that HCP, caregivers and MCI participants are willing to adopt eHealth solutions based on wearables in order to assist in daily care through holistic and objective monitoring as well as the difficulties, peculiarities and priorities they are aspiring to alleviate through such systems.

A retail sector is a group of organization or people who sell goods or services for gaining income. Fraud is wrongful or criminal activities for the economic and personal benefits. Fraud detection is finding actual or expected fraud which takes place in an organization and in the retail market is one of the challenging aspects. Fraud is mischievous activities occur in retail sector includes shoplifting, skimming, replicating cards from skimmed data, counterfeiting, bar code or POS(Point-of-Sale) manipulation, contamination, mislabeling, substitute cheaper ingredients instead of high-quality ingredients. Fraudulent activities occur in the retail sector by both consumer and supplier. Analyzing financial crimes related to fraudulent activities is difficult where traditional data mining techniques fail to address all of them. Big data analytics is used to identify an unusual pattern to detect and prevent fraud in the retail sector. Various predictive analytics tools are used to handle massive data and their pattern.

Integrated view of energy markets leads to the introduction of new market participants named Energy Retailer (E.R). The competition between E.Rs in an integrated market, significantly improves the market performance in different aspects such as sustainability, security of supply and environmental concerns. In this paper, the performance of the E.R has been evaluated in an integrated market considering the emission penalty reduction in the objective function. The performance of the E.R has been evaluated in different market structures comparing the share of the E.R in energy trading and the carbon emissions in multi-carrier energy system.

The retail electricity market is becoming prosperous in China in the recent years. However, due to many uncertain factors, the retail providers may go bankrupt and have to withdraw from the retail market, thus the customers will suffer from unexpected supply interruption and economic loss, which will disturb the healthy operation of the electricity market. The purpose of the last resort service is to take the responsibility of the bankrupted providers, and maintain the power supply to the customers. This service is often provided by retail electric providers in foreign countries while undertaken by the power grid companies in China. In this paper, the experiences of the last resort service in several foreign countries are investigated. The qualification, pricing, customer allocation, and operating process about the last resort service are investigated. The benefit and risk of the last resort service are analyzed to determine the sharing of the responsibilities, interests, and punishments among the participants of the retail market. Based on the investigation, some practical suggestions to the last resort service are given to the provincial retail market in China.

In order to help the residential customers participate in the electricity markets, a new entity called load aggregator (LA) is emerged. The load aggregator (LA) bids in the wholesale market to purchase electricity and meet the expected demand of its customers in the retail market. Because of the uncertainty of the wholesale market price (WMP), the LA has to undertake all the risk caused by the price volatility in the wholesale market. Although some existing literatures take the uncertainty of WMP into account, most of them determine a constant energy bid for each trading period regardless of the WMP. To this end, this paper proposes a scenario-based optimal bidding strategy model for the LA that implements the demand response program (DRP), which enables the LA to reduce the risk of profit loss caused by price volatility. Moreover, with the implementation of the DRP, not only can the LA bids in the wholesale market, but also it is able to aggregate the demand response (DR) resources from its customers and provide DR service to the power system operator such as the independent system operator (ISO) or regional transmission organization (RTO). The case study using a dataset from the Thames valley vision (TVV) verifies the effectiveness of the proposed model.

The paper shows the functional architecture and the main components of the simulation model of the new electricity market in Ukraine. The developed mathematical model is realized in the Simulation electricity market model, which covers such segments of the market as day-ahead market, balancing and retail market. The simulation model allows you to calculate prices and tariffs taking into account the influence of fuel markets and macroeconomic indicators of the Ukrainian economy. Additionally, the model reflects the structure of prices and tariffs for both transmission and distribution companies, as well as for “universal services” providers and electricity supplier.

Shopping festivals such as Black Friday are an important period for online retail businesses. Shopping behaviors during these periods tend to be rushed and time-bounded due to many short-lived promotions, which makes it challenging for businesses to expect traffic and provision their service. In this paper, we examine the click action logs of five million shoppers from a large online retail market during a mega sale event in China and present a model of shopper behavior. Our model takes into account a wide range of features including the product category, time of day, and click sequences. Our model could identify precursors of purchase from as early as two weeks prior to the shopping festival. These findings help online retailers better prepare for future events by efficiently classifying shoppers and thereby reducing the operational costs associated with provisioning any rushed shopping moments.

Electric vehicles are an integral part of futuristic smart grids. Electric vehicles give rise to a new player in the retail market called as aggregator. This paper proposes an intelligent charging scheduling problem for an Electric Vehicle (EV) aggregator considering vehicle-to-grid (V2G) and grid-to-vehicle (G2V) capabilities with an objective to minimize the total charging cost. Since electricity price at the charging node may be subject to uncertainties, Information Gap Decision Theory (IGDT) is proposed in this paper to handle uncertainties in the price. The original intelligent charging scheduling problem is non-linear. The paper proposes a modified Mixed Integer Linear Programming (MILP) based reformulation and solves with CPLEX using GAMS as an aggregator.

The penetration of renewable resources in the wholesale electricity market and the demand response in the retail market cause the demand and the supply to become more unpredictable. The ISO is hard to efficiently schedule the production and dispatch the demand. Furthermore, strategic bidding in a more competitive environment is an important problem for the generator. Forecasting the hourly market clearing price (MCP) in the day-ahead electricity market is one of essential task for any bidding decision making. But only a single predicted value of MCP cannot offer enough help for the generator to select the optimal bidding strategies. Aiming at challenge these tasks, we design a new wholesale mechanism in which the ISO declares an interval demand to the wholesale market. The interval demand is more robust than a single demand figure and enables the ISO to handle unpredictable demand under the DR programs. We also developed a forecasting model to forecast a MCP function under the interval demand and introduce the notion of confidence interval to the forecasting model. The confidence interval predicts the exact range of hourly MCP. Based on these work, the optimal bidding strategies for the generator under an interval demand is also illustrated.

The advancement of smartphone technology and the penetration within the market makes these devices an incredible medium for retailers to engage with their customers. Bluetooth Low Energy (BLE) extends the capabilities of smartphones even further and thus provides the potential for retailers to offer services to customers based on their location that is challenging with conventional techniques. Before embarking on developing solutions, it is vital to understand the factors that influence consumers to embrace this technology and ensure retailers can offer appropriate services to their customers. While a number of studies have discussed BLE, very little have examined it from the consumers' perspective. This study extends the UTAUT2 model with ‘privacy concern’ and explores the factors influencing consumers' behaviour towards proximity sensors within a retail store. The results confirm that all the factors except for ‘price value’ are statistically significant.

This study is based on the analysis of the sales of the retail market in certain parts of Mumbai. Factor analysis is done on the data collected to find out the most important factors affecting sales in the region. The insights drawn from this study will be of importance to a new retailer interested to start business in this region as it provides an outlay of the important know-how's before entering the business.

This paper presents the use, benefits and challenges of using of the Internet of Things (IoT) in retail businesses. The paper aims to inform Chief Information Officers (CIOs), digital strategists or Head of Information Technology in retail of the use, benefits and challenges of using IoT. IoT is a new paradigm that uses different technologies to achieve various tasks. The use of IoT is growing among business and individuals and businesses that are not currently using IoT are set to invest in its use in the coming years. The use of IoT is increasing in the retail market, giving to the vast benefits that IoT offers. For businesses to fully use IoT they will need to change their business processes to realise its benefits. However, businesses to fully realise the benefits of IoT they need to overcome the challenges that it presents, this includes but not limited to security, privacy and network challenges. The Technology Organisation Environment (TOE) Framework was used to study the use, benefit and challenges of using IoT in retail businesses.

Agent Based Simulation has enabled economist tomodel and analyse strategic decisions of participants as wellas political decision markers by using social-behavioural andeconomic technical aspects simultaneously. While Agent-basedSimulation has had a significant contribution to the study theenergy market by allowing economists and researchers to modelthe complexity and dynamics of real electricity markets, it hasproved to be limited in some ways: Agent-based models donot involve methodological aspects for enabling the creation ofmodels from a participative multi-disciplinary perspective. Inmany cases, modellers (programmers, economists or researchers) need to assume the knowledge of multiple domain areas fordesigning and implementing the models, they must have theability to handle different domain such as sociology, economyand computer science. This paper shows how the use of anOntology Driven Model approach in conjunction with methodsfrom the Agent Computational Economics and other comple-mentaries methodological aspects can contribute to improve such shortcomings.

As the retail market for information and communications technology (ICT) becomes increasingly competitive, managers and marketers face the challenge of finding ways to motivate consumers to pay more for ICT products. Studies have examined the concept of luxury in the context of retail markets; however, few have investigated how the concept applies to the ICT market. This empirical study shows that from the consumer's perspective, high-priced ICT products fall into at least two categories-luxury products and high-end products. Consumers who adopt perfection-seeking, novelty-seeking, and prestige-seeking shopping styles tend to prefer a luxury product; in addition, the consumer who desires uniqueness is likely to adopt the prestige-seeking shopping style. Consumers who adopt innovation-seeking and perfection-seeking shopping styles tend to prefer a high-end product; innovation-seeking customers are more likely to pay attention to utilitarian values and accept new brands. This study aims to help ICT firms develop and market their products.

In the oil retail market, to participate into the whole oil sale process is an urgent demand for oil retail company. As a respond to this situation, a GPRS based remote monitoring and controlling system for oil delivery truck is proposed in this paper. By implement the remote monitoring and controlling system, the oil retail company can fully monitor and control the whole oil sale process, and reduce unnecessary expense.

We consider a vertical industry where a dominant upstream producer (or a cartel of producers) sells an homogenous good to retailers who serve the final market. Using an industrial organisation approach, we show that if at an initial stage the final market is supplied by a single incumbent firm holding long-term contracts with the dominant upstream producer, then an excessive entry of competitors on the retail market leads to an increase of the price paid by final consumers. Our model illustrates the existence of a trade-off between alleviating the double marginalisation issue thanks to additional competition at the retail level and limiting the market power of upstream producers on the wholesale market through long-term contracts. Our results still hold if the dominant upstream firm faces a competitive fringe, provided firms of the fringe face substantially higher costs of production. The counter-intuitive outcome of our model could be of particular relevance for the European natural gas industry, in case the recently settled organisation grouping the largest gas exporters was to turn into a cartel of producers.

Electricity retail market models applied across Europe differ significantly from each other. The models used in the Nordic countries are not similar either. Some aspects of them will, however, experience harmonization because of the aim of a common Nordic end-user market for electricity. Lately, there has also been discussion about a need to harmonize models all over Europe. This paper presents the current features of retail market models applied in the Nordic countries and discusses the future changes that may occur. Also Nordic industry's views concerning retail market models are discussed.

Nowadays with the rapid development of knowledge-based economy, people want to get more knowledge. Facing this big market, more and more booksellers hope the new way to expand their business. Because of this goal, E-business as the new style is appeared to fit this situation. It’ s important to research the effect of E-business to both sellers and buyers in the book retail market. This article will analyze the effect of E-business mainly by SWOT method from sellers’ angle, and will bring forward some countermeasures.

In liberalized telecommunications markets, the incumbent usually has to investment to improve the service quality. The incumbentpsilas incentives to invest in a network technology that increases consumerspsila willingness to pay for services is investigated in a model of a vertically integrated monopolist network provider who faces rival operators in the retail market. We find that different from the conclusion obtained before the spillovers have positive effect and regulation has negative effect on investment level.

The Finnish retail electricity market has been fully open for the past ten years. However, it has been estimated that still about 70 per cent of household customers buy electricity from their local incumbent supplier. The Finnish customers have been less active than their Norwegian and Swedish counterparts, although the retail markets were opened approximately at the same time in these three countries. In each country, there still exists several barriers to competition. One of the most significant ones is the lack of customer activity. At present, the Nordic energy regulators are making efforts towards an integrated Nordic retail market in order to increase competition. The increased amount of suppliers alone will hardly be enough to increase competition. The main focus should be in improving customer activity.

An important problem in Association Rule (AR) mining is the identification of interesting ARs. In a retail market basket context, items may be related through various relationships like mutual interaction, `substitutability? and `complementarity ?. We deJine them and present a classification of these relationships. We propose `Item-Relatedness' of an item-pair as a composite of these relationships. We then present a structural decomposition of the relatedness of an item pair, based on its co-occurring transactions, co-occurring and non co-occurring item-neighborhoods. We identify those relationships that can be discerned solely from transaction data analysis. ARs that contain unrelated or weakly related item-pairs are likely to be interesting. The structural decomposition helps in clarifying components of relatedness. We finally analyze a typical scenario that contains objects revealing various shades of relatedness.

Information concerning the consumer's load profile (LP) in a deregulated environment is of initial importance for load balancing purposes and for billing the consumers, which deviated from contracted schedules. Establishment of a system that enables consumers, without the quarter-hourly load measurements, to participate at the retail market, requires the typical load profiles (TLP) of the various types of consumers. Furthermore, simple and straightforward method for the TLP assignment to the particular eligible consumers should be established. In the paper, a methodology for consumer's load profiles classification is presented. The measured load profiles (MLP) are classified by hierarchic clustering method and fuzzy logic. Probability neural network is used to assign the TLP to the particular group of consumers. The results demonstrate that the proposed method is efficient for and assigning TLP to the consumers.

Portugal has opened its' retail electricity market for new entrants, while removing the burdens for competition among the supplier and customers' switching between the electricity suppliers. The ultimate objective of this liberalization practice is to sharpen competition in the retail segment of the electricity market. In this paper, we assess the success of the liberalization process in the retail sector of the Portuguese electricity market by studying the customers' behavior, changes in the retail rates and the market concentration. Finally, we provide some explanations for the high switching rates of the customers despite the increase in the retail rates. We also explain why the retail rates are not following the changes of the wholesale prices and the reason for the high market concentration in the retail segment.