Real Pricing Research Articles

REAL OPTION AND VERTICAL MIXED-USE DEVELOPMENT

Vertical mixed-use development is a favourite choice in urban development in high-density Asian cities to increase the land use efficiency. The flexibility of construction timing and the restrictions by lease contracts in vertical mixeduse projects are usually different from horizontal ones and single-use properties. To improve the valuation for vertical mixed-use projects, this study re-examines the real option pricing model. Simultaneous development for different uses and a finite maximum waiting period are the major characteristics of these projects. An approach is introduced to determine whether to develop a mixed-use project vertically or horizontally on the basis of a statistics called the critical height premium. The vertical mixed-use project pricing model can be further verified by containing a height premium if market price information is derived from non-vertical mixed-use properties. This study suggests a more comprehensive real option approach to quantify the advantages and disadvantages of operating vertical mixed-use developments.

Decoupled building-to-transmission-network for frequency support in PV systems dominated grid

Tendency towards installation of distributed photo-voltaic (PV) systems has led to an increased emphasis on grid frequency control. Grid-interactive buildings equipped with heating ventilation and air conditioning (HVAC) system has a great potential to regulate the frequency in renewable energy resources rich grids. This paper integrates distributed PV systems in a decoupled building-to-transmission-network (B2TN) and explicitly formulates the interaction between a grid and the buildings through real time pricing (RTP). As grid frequency conveys the information about operating conditions of a power system, therefore, frequency based RTP generators namely; linear, hyperbolic tangent and inverse hyperbolic tangent are used. A price responsive model predictive controller (MPC) for optimal scheduling of HVAC load is developed where reference temperature set point is dynamically adjusted subject to RTP. Moreover, the clear and cloud covered sky impacts of PV power generation on frequency and RTP deviations are investigated. Comparatively, inverse hyperbolic tangent model maps the frequency deviations on a wider RTP range which increases the scale of reference temperature set point resulting peak frequency deviation suppression up to 40% and load regulation beyond 12 MW without inherently affecting the building electricity cost when compared to ordinary MPC (without demand response service).

Redevelopment Strategies and Building Ages

An improved real option pricing model that incorporates the depreciation effect for two-stage redevelopment projects will be demonstrated to estimate the redevelopment project values in a wide range of scenarios. Based on a stochastic differential equation, numerical analyses will focus on new factors that influence the expected exercise time, that is, depreciation rate, the annual increase in average building age, building age of the targeted property, and the average building ages in the same region. The depreciation rate is the most influential factor in this model. More importantly, this study will summarize three exercise strategies that cover all combination of the parameters. In some cases, with a low capital return rate, or high depreciation rate, or both, the traditional simultaneous exercise strategy based on the optimal demolition price-to-cost ratio is not feasible. Instead, either a sequential exercise or simultaneous exercise that is based on the optimal rebuilding price-to-cost ratio is the best choice. Detailed procedures on how to adopt the best strategy will be demonstrated. The acquisition price of the old property proved to be sensitive to the depreciation rate and the capital return rate within a certain range. To apply this model properly, the feasible time interval for the traditional simultaneous exercise strategy and the depreciation rate should be estimated accurately.

Shedding Light on the Factors That Influence Residential Demand Response in Japan

Residential demand response empowers the role of electricity consumers by allowing them to change their patterns of consumption, which can help balance the energy grid. Although such type of management is envisaged to play an increasingly important role in the integration of renewables into the grid, the factors that influence household engagement in these initiatives have not been fully explored in Japan. This study examines the influence of interpersonal, intrapersonal, and socio-demographic characteristics of households in Yokohama on their willingness to participate in demand response programs. Time of use, real time pricing, critical peak pricing, and direct load control were considered as potential candidates for adoption. In addition, the authors explored the willingness of households to receive non-electricity related information in their in-home displays and participate in a philanthropy-based peer-to-peer energy platform. Primary data were collected though a questionnaire survey and supplemented by key informant interviews. The findings indicate that household income, ownership of electric vehicles, socio-environmental awareness, perceived sense of comfort, control, and complexity, as well as philanthropic inclinations, all constitute drivers that influence demand flexibility. Finally, policy recommendations that could potentially help introduce residential demand response programs to a wider section of the public are also proposed.

A Review on Demand Side Management Applications, Techniques, and Potential Energy and Cost saving

Electrical peak load demand all over the world is always anticipated to grow, which is challenging electrical utility to supply such increasing load demand in a cost effective, reliable and sustainable manner. Thus, there is a need to study some of load management (LM) techniques employed to minimize energy consumption, reduce consumers' electricity bills and decrease the greenhouse gas emissions responsible for global warming. This paper presents a review of several recent LM strategies and optimization algorithms in different domains. The review is complemented by tabulating several demand side management (DSM) techniques with a specific view on the used demand response (DR) programs, key finding and benefits gained. A special focus is directed to the communication protocols and wireless technology, incorporation of renewable energy resources (RERs), battery energy storage (BES), home appliances scheduling and power quality applications. The outcome of this review reveals that the real time pricing (RTP) is the most efficient price-based mechanism program (PBP), whilst time of use (TOU) is the basic PBP and easiest to implement. Energy efficiency programs have proved the highest influential impact on the annual energy saving over the other dynamic pricing mechanism programs. Through a forecasted proposal of future study, DSM proved tremendous potential annual energy savings, peak demand savings, and investment cost rates within different consumption sectors progressively up to year 2030.

Giving consumers too many choices: a false good idea? A lab experiment on water and electricity tariffs

Electricity and water tariffs are undergoing significant changes due to smart metering, retail competition, and regulatory changes. Consumers now have to choose between different tariffs which are getting more and more complex. Theoretically, these new tariffs aim to use more cost-reflective pricing to incentivise consumers to adopt the right behaviours. However, empirical evidence from real pricing shows that consumers are confused by the complexity. Based on a lab experiment, this paper investigates how electricity and water consumers adopt more or less complicated tariffs and adapt their behaviours accordingly. We show that subjects prefer simple tariffs over complex ones. However, when they receive adequate information about tariffs and appropriate behaviours, they choose more complex tariffs. These results argue in favour of self-selection of tariff forms, in order to account for consumers’ different abilities to respond to the price signal. Lastly, we discuss the appropriateness of using a price mechanism to incentivise consumers.

A novel methodology for optimal sizing photovoltaic-battery systems in smart homes considering grid outages and demand response

–This paper deals with the optimal sizing of a hybrid photovoltaic-battery storage system for home energy management considering reliability against grid outages and demand response. To that end, a novel optimization framework is developed which aims at minimizing the electricity bill while the reliability of the system is ensured for certain common outages. In order to ensure the accuracy of the results, a large amount of characteristic outages along with demand, solar irradiance and temperature profiles are generated from real data. Clustering techniques are used for reducing this data to those most characteristics profiles and manage with the unpredictable behaviour of the outage events. Demand response is incorporated via different incentives like tariffs based on time of use and real time pricing, along with the optimal scheduling of different typical deferrable appliances. A case study on a smart-prosumer environment serves to illustrate the capabilities of the developed approach as providing sufficient guidelines for its universal applicability. Different cases studies are simulated considering different battery technologies and electricity tariffs for comparison. Various aspects related with the reliability against grid outages are also analysed like its impact on the project cost or the influence of demand response strategies.

Energy management of a microgrid using demand response strategy including renewable uncertainties

Abstract Microgrid is an effective means of integrating multiple energy sources of distributed energy to improve the economy, stability and security of the energy systems. A typical microgrid consists of Renewable Energy Source (RES), Controllable Thermal Units (CTU), Energy Storage System (ESS), interruptible and uninterruptible loads. From the perspective of the generation, the microgrid should be operated at the minimum operating cost, whereas from the perspective of demand, the energy cost imposed on the consumer should be minimum. The main key in controlling the relationship of microgrid with the utility grid is managing the demand. An Energy Management System (EMS) is required to have real time control over the demand and the Distributed Energy Resources (DER). Demand Side Management (DSM) assesses the actual demand in the microgrid to integrate different energy resources distributed within the grid. With these motivations towards the operation of a microgrid and also to achieve the objective of minimizing the total expected operating cost, the DER schedules are optimized for meeting the loads. Demand Response (DR) a part of DSM is integrated with MG islanded mode operation by using Time of Use (TOU) and Real Time Pricing (RTP) procedures. Both TOU and RTP are used for shifting the controllable loads. RES is used for generator side cost reduction and load shifting using DR performs the load side control by reducing the peak to average ratio. Four different cases with and without the PV, wind uncertainties and ESS are analyzed with Demand Response and Unitcommittment (DRUC) strategy. The Strawberry (SBY) algorithm is used for obtaining the minimum operating cost and to achieve better energy management of the Microgrid.

Enterprise Value Evaluation Based on Fuzzy Real Option Pricing Model

With the continuous progress and development of my country's market economy system, my country's market economy is showing unprecedented prosperity. More and more companies are appearing in market competition to achieve common progress and development with the market economy. Enterprise value is the basis for an enterprise to survive and develop in the market, and it is also an important basis for investors to integrate funds. The addition of fuzzy numerical theory is conducive to scientific and effective enterprise value evaluation results. This article mainly starts from studying the theory of real option pricing model and analyzes the definition of fuzzy real option pricing model. Through the analysis of the status quo of fuzzy real option pricing models, we summarize the shortcomings of model theory in practical application, and put forward feasibility opinions or suggestions for fuzzy real option pricing models, so as to better conduct corporate value evaluation work and increase the effectiveness of evaluation results Sex. Enabling corporate investors and managers to grasp corporate value data, so as to make scientific and reasonable investment.

A Fair Pricing Mechanism in Smart Grids for Low Energy Consumption Users

Based on energy demand, consumers can be broadly categorized into low energy consumers (LECs) and high energy consumers (HECs). HECs use heavy load appliances, e.g., electric heaters and air conditioners, and LECs do not use heavy load appliances. Thus, HECs demand more energy compared to LECs. The usage of high energy consumption appliances by HECs leads to peak formation in various time intervals. Different pricing schemes, i.e., time of use (ToU), real time pricing (RTP), inclined block rate (IBR), and critical peak pricing (CPP), have been proposed previously. In ToU, an energy tariff is divided into three blocks, i.e., on-peak (high rates), off-peak (low rates), and mid-peak (between on-peak and off-peak rates) hours, and these rates are applied to all electricity users without distinction. The high energy demand by HECs causes the high peak formation; thus, higher rates should be applied to only HECs rather than all consumers, which is not the case in existing billing mechanisms. LECs are also charged higher rates in on-peak intervals and this billing mechanisms are unjustified. Thus, in this paper, a fair pricing scheme (FPS) based on power demand forecasting is developed to reduce extra bills of LECs. First, we developed a machine learning-based electricity load forecasting method, i.e., an extreme learning machine (ELM), in order to differentiate LECs and HECs. With the proposed FPS, electricity cost calculations for LECs and HECs are based on the actual energy consumption; thus, LECs do not subsidize HECs. Simulations were conducted for performance evaluation of our proposed FPS mechanism, and the results demonstrate LECs can reduce electricity cost up to 11.0075%, and HECs are charged relatively higher than previous pricing schemes as a penalty for their contribution to the on-peak formation. As a result, a fairer system is realized, and the total revenue of the utility company is assured.

Cognitive radio enabled smart meter based on real time pricing system using hybrid FPA-GSA algorithm

Cognitive radio enabled smart meter based on real time pricing system using hybrid FPA-GSA algorithm

Cognitive radio enabled smart meter based on real time pricing system using hybrid FPA-GSA algorithm

Cognitive radio enabled smart meter based on real time pricing system using hybrid FPA-GSA algorithm

A holistic review of research studies on financial risk management in public–private partnership projects

PurposeGlobally, the management of financial risks has gained much attention in the public–private partnerships (PPP) market in recent years. Existing studies rank financial risks among the topmost risk factors that determine the success or failure of a PPP project. As essential for managing financial risks, a systematic review of previous studies on financial risk management of PPP from 1995 to 2019 (inclusive of both years) has been presented in this paper.Design/methodology/approachThe paper undertakes a systematic analysis of 49 relevant and available studies on financial risk management of PPP projects.FindingsFrom the results, high-interest charges, increased construction costs and increased market risks are some of the key financial risks hampering the success of PPP projects. Techniques used to assess financial risks include Monte Carlo Simulation (MCS) and Net Present Value (NPV). Financial risks control adopted by project managers include minimum revenue guarantee and real option pricing. Extremely limited studies on financial risk management in PPP projects in developing economies was revealed.Practical implicationsProject managers in developing financial risk management models may use the outcome of this paper to improve the financial success of PPP projects. Holistically, researchers will be guided to investigate and heighten the pertinent issues on financial risk management of PPP projects in academia.Originality/valueThe results provide a rare guide to project managers in controlling financial risks of PPP projects which is an unexplored topic. It is also the first paper to highlight the issues of financial risk management in PPP projects research.

A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network

This study summarizes a critical review on EVs’ optimal charging and scheduling under dynamic pricing schemes. A detailed comparison of these schemes, namely, Real Time Pricing (RTP), Time of Use (ToU), Critical Peak Pricing (CPP), and Peak Time Rebates (PTR), is presented. Globally, the intention is to reduce the carbon emissions (CO2) has motivated the extensive practice of Electric Vehicles (EVs). The uncoordinated charging and uncontrolled integration however of EVs to the distribution network deteriorates the system performance in terms of power quality issues. Therefore, the EVs’ charging activity can be coordinated by dynamic electricity pricing, which can influence the charging activities of the EVs customers by offering flexible pricing at different demands. Recently, with developments in technology and control schemes, the RTP scheme offers more promise compared to the other types of tariff because of the greater flexibility for EVs’ customers to adjust their demands. It however involves higher degree of billing instability, which may influence the customer’s confidence. In addition, the RTP scheme needs a robust intelligent automation system to improve the customer’s feedback to time varying prices. In addition, the review covers the main optimization methods employed in a dynamic pricing environment to achieve objectives such as power loss and electricity cost minimization, peak load reduction, voltage regulation, distribution infrastructure overloading minimization, etc.

The Value of Design in Real Estate Asset Pricing

Does design contribute to real estate value? Practicing architects require evidence to justify both functional and aesthetic building needs within the financial ecosystem. Some buildings that become real estate assets are valued using models that consider some proxies for understanding value, but these features are abstract and may misidentify the sub-optimal differentiation that design brings. The lack of feedback between real estate valuation and building design often leads to poor design and economic outcomes. To address this miscommunication, we investigate the transaction price performance of four external architectural form features—diagonality, curvature, setbacks and podiums. Whilst controlling for drivers that are known to explain transaction price variation, we find that diagonality and podiums have a positive pricing differential of 12.4 and 9.7% more than rectilinear control buildings, respectively. Buildings with setbacks have a negative pricing differential of 10%. Furthermore, these results are also consistent for rental valuation. Results suggest that there is a significant economic impact of some architectural form interventions that differentiate commercial buildings and contribute to the role of form in design, planning and commercial real estate.

Online fuzzy control of HVAC systems considering demand response and users’ comfort

ABSTRACT Heating, ventilation and air conditioning (HVAC) systems play an essential role in demand response (DR) programs. In this paper, a fuzzy controller is designed to adjust the HVAC set-points optimally. The aims of the designed controller are multifold: to save energy, to improve user’s comfort, and reduce HVAC electricity costs. In the current research, indices such as daily energy cost, minimum and maximum home temperature, energy usage, energy usage during peak hours, and user’s comfort are proposed and discussed for the evaluation of HVAC function. In addition, the effect of different pricing schemes such as fixed pricing (FP), time-of-use pricing (TOU), and real-time pricing (RTP), are analyzed. Further, the adaptability of the proposed model enabled us to investigate users with different attitudes toward welfare and cost. Finally, the effects of set-point and dead-band width are discussed. The results show that the proposed controller reaches the pre-determined aims successfully. Abbreviations: HVAC: Heating, ventilation, and air conditioning; FP: Fixed Pricing; TOU: Time Of Use; RTP: Real Time Pricing; PR: The HVAC system energy consumption cost in a day; UC: The total time that the user experiences an uncomfortable situation in a day; T_min: The minimum RealFeel Temperature experienced in a day by the user; T_max: The maximum RealFeel Temperature experienced in a day by the user; ECP: Energy consumed by the HVAC system during peak hours; EC: Energy consumed by the HVAC system during a day

Development of Home Energy Management Scheme for a Smart Grid Community

The steady increase in energy demand for residential consumers requires an efficient energy management scheme. Utility organizations encourage household applicants to engage in residential energy management (REM) system. The utility’s primary goal is to reduce system peak load demand while consumer intends to reduce electricity bills. The benefits of REM can be enhanced with renewable energy sources (RESs), backup battery storage system (BBSS), and optimal power-sharing strategies. This paper aims to reduce energy usages and monetary cost for smart grid communities with an efficient home energy management scheme (HEMS). Normally, the residential consumer deals with numerous smart home appliances that have various operating time priorities depending on consumer preferences. In this paper, a cost-efficient power-sharing technique is developed which works based on priorities of appliances’ operating time. The home appliances are sorted on priority basis and the BBSS are charged and discharged based on the energy availability within the smart grid communities and real time energy pricing. The benefits of optimal power-sharing techniques with the RESs and BBSS are analyzed by taking three different scenarios which are simulated by C++ software package. Extensive case studies are carried out to validate the effectiveness of the proposed energy management scheme. It is demonstrated that the proposed method can save energy and reduce electricity cost up to 35% and 45% compared to the existing methods.

Economic impact of monetary policy: Focus on real estate sector in Italy

AbstractThis study investigates the nexus between financial market and real estate (RE) sector against the backdrop of ECB's unconventional monetary policy. A financial dynamic computable general equilibrium (DCGE) model is calibrated on the financial social accounting matrix (FSAM) of Italian economy. The findings confirm that the inclusion of financial intermediation into real economy affects the real estate sector's output, value added, and pricing.

A Novel Algorithm for Day-Ahead Real Time Pricing for Energy Management in Smart Grid

A Novel Algorithm for Day-Ahead Real Time Pricing for Energy Management in Smart Grid

A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid

In most demand response (DR) based residential load management systems, shifting a considerable amount of load in low price intervals reduces end user cost, however, it may create rebound peaks and user dissatisfaction. To overcome these problems, this work presents a novel approach to optimizing load demand and storage management in response to dynamic pricing using machine learning and optimization algorithms. Unlike traditional load scheduling mechanisms, the proposed algorithm is based on finding suggested low tariff area using artificial neural network (ANN). Where the historical load demand individualized power consumption profiles of all users and real time pricing (RTP) signal are used as input parameters for a forecasting module for training and validating the network. In a response, the ANN module provides a suggested low tariff area to all users such that the electricity tariff below the low tariff area is market based. While the users are charged high prices on the basis of a proposed load based pricing policy (LBPP) if they violate low tariff area, which is based on RTP and inclining block rate (IBR). However, we first developed the mathematical models of load, pricing and energy storage systems (ESS), which are an integral part of the optimization problem. Then, based on suggested low tariff area, the problem is formulated as a linear programming (LP) optimization problem and is solved by using both deterministic and heuristic algorithms. The proposed mechanism is validated via extensive simulations and results show the effectiveness in terms of minimizing the electricity bill as well as intercepting the creation of minimal-price peaks. Therefore, the proposed energy management scheme is beneficial to both end user and utility company.