Real Pricing Research Articles

Software Defined Networks-Based Smart Grid Communication: A Comprehensive Survey

The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid also known as, smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication (SGC) system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SGC system is built on different vendor specific devices and protocols. Therefore, the current SG systems are not protocol independent, thus leading to interoperability issue. Software defined network (SDN) has been proposed to monitor and manage the communication networks globally. By separating the control plane from the data plane, SDN helps the network operators to manage the network flexibly. Since SG heavily relies on communication networks, therefore, SDN has also paved its way into the SG. By applying SDN in SG systems, efficiency and resiliency can potentially be improved. SDN, with its programmability, protocol independence, and granularity features, can help the SG to integrate different SG standards and protocols, to cope with diverse communication systems, and to help SG to perform traffic flow orchestration and to meet specific SG quality of service requirements. This paper serves as a comprehensive survey on SDN-based SGC. In this paper, we first discuss taxonomy of advantages of SDN-based SGC. We then discuss SDN-based SGC architectures, along with case studies. This paper provides an in-depth discussion on routing schemes for SDN-based SGC. We also provide detailed survey of security and privacy schemes applied to SDN-based SGC. We furthermore present challenges, open issues, and future research directions related to SDN-based SGC.

Valuing strategic investments under stochastic interest rates: A real option approach

One of the most challenging issues in management is the valuation of strategic investments. In particular, when undertaking projects such as an expansion or the launch of a new brand, or an investment in R&D and intellectual capital, which are characterized by a long-term horizon, a firm has also to face the risk due to the interest rate. In this work, we propose to value investments subject to interest rate risk using a real options approach (Schulmerich, 2010). This task requires the typical technicalities of option pricing, which often rely on complex and time-consuming techniques to value investment projects. For instance, Schulmerich (2010) is, to the best of our knowledge, the first work where the interest rate risk is considered for real option analysis. Nevertheless, the valuation of investment projects is done by employing binomial trees, which are computationally very expensive. In the current paper, a different modeling framework (in continuous-time) for real option pricing is proposed which allows one to account for interest rate risk and, at the same time, to reduce computational complexity. In particular, the net present value of the cash inflows is specified by a geometric Brownian motion and the interest rate is modeled by using a process of Vasicek type, which is calibrated to real market data. Such an approach yields an explicit formula for valuing various kinds of investment strategies, such as the option to defer and the option to expand. Therefore, the one proposed is the first model in the field of real options that accounts for the interest rate risk and, at the same time, offers an easy to implement formula which makes the model itself very suitable for practitioners. An empirical analysis is presented which illustrates the proposed approach from the practical point-of-view and highlights the impact of stochastic interest rates in investment valuation.

Compound simulated annealing optimization algorithm for real time electricity pricing

Compound simulated annealing optimization algorithm for real time electricity pricing

Cheap TIPS or Expensive Inflation Swaps?: Mispricing in Real Asset Markets

Inflation risks are explicit in either (i) the nominal pricing of real payoffs in which prices are denominated in dollars, or (ii) the real pricing of nominal payoffs in which prices are denominated in consumption baskets. While the former involves over-the-counter inflation-indexed contracts of real asset market, the latter involves exchange-traded and highly liquid contracts of nominal asset market. We employ a parametric pricing model to investigate the asymmetry between these two markets. The model obtains a liquidity-free distribution of future inflation using new price data of T-note futures in nominal asset market, and implies liquidity risk premia separately for any traded contract in real asset market. These premia indicate both an underpricing for TIPS and an overpricing for inflation swaps, whose significances increase with the tenor of these assets. Such a mispricing in inflation swaps help temper a severe implied mispricing of TIPS needed to match the puzzling trade profit on the nominal-TIPS yield spread. While yields on TIPS still command a liquidity component, this finding implicates less pronounced borrowing costs to the U.S. government in issuing TIPS.

Operation Scheduling of Household Load, EV and BESS Using Real Time Pricing, Incentive Based DR and Peak Power Limiting Strategy

Abstract Demand response (DR) programs have become powerful tools of the smart grids, which provide opportunities for the end-use consumers to participate actively in the energy management programs. This paper investigates impact of different DR strategies in a home-energy management system having consumer with regular load, electric vehicle (EV) and battery-energy storage system (BESS) in the home. The EV is considered as a special type of load, which can also work as an electricity generation source by discharging the power in vehicle-to-home mode during high price time. BESS and a small renewable energy source in form of rooftop photovoltaic panels give a significant contribution in the energy management of the system. As the main contribution to the literature, a mixed integer linear programming based model of home energy management system is formulated to minimize the daily cost of electricity consumption under the effect of different DR programs; such as real time price based DR program, incentive based DR program and peak power limiting DR program. Finally, total electricity prices are analysed in the case studies by including different preferences of the household consumer under mentioned DR programs. A total of 26.93 % electricity cost reduction is noticed with respect to base case, without peak limiting DR and 19.93 % electricity cost reduction is noticed with respect to base case, with peak limiting DR.

A novel pricing scheme for managing virtual energy communities and promoting behavioral change towards energy efficiency

The harmonization between the variable rate of energy production in the era of massive renewable energy penetration is a major challenge in an open, competitive and resilient electricity market. As a result, there is an increasing need for modern pricing schemes, which will effectively incentivize willing users to modify their energy consumption pattern to meet this objective. Current energy pricing schemes (e.g. real time pricing) treat all users the same, and do not adequately compensate for behavioral changes, thus mitigating the behavioral change dynamics. In this paper, we propose a Community Real Time Pricing (CRTP) scheme together with an Energy Community Formation Algorithm (ECFA), where users are clustered in Virtual Energy Communities (VECs) according to: (i) their level of flexibility in modifying their Energy Consumption Curve (ECC), and (ii) their relationships in Online Social Networks (OSNs), modelling peer-pressure capabilities. We show that CRTP with ECFA can simultaneously achieve considerable reduction in the system’s energy cost and greater aggregated users’ welfare than with the state-of-the-art real time pricing. CRTP–ECFA adopts a truly fair pricing policy, as each user is rewarded exactly according to his/her individual contribution in reducing system costs, thus promoting the desired behavioral change.

Personalized real time pricing for efficient and fair demand response in energy cooperatives and highly competitive flexibility markets

This paper contributes to the well-known challenge of active user participation in demand side management (DSM). In DSM, there is a need for modern pricing mechanisms that will be able to effectively incentivize selfishly behaving users in modifying their energy consumption pattern towards system-level goals like energy efficiency. Three generally desired properties of DSM algorithms are: user satisfaction, energy cost minimization and fairness. In this paper, a personalized–real time pricing (P-RTP) mechanism design framework is proposed that fairly allocates the energy cost reduction only to the users that provoke it. Thus, the proposed mechanism achieves significant reduction of the energy cost without sacrificing at all the welfare (user satisfaction) of electricity consumers. The business model that the proposed mechanism envisages is highly competitive flexibility market environments as well as energy cooperatives.

Energy cost minimization for unrelated parallel machine scheduling under real time and demand charge pricing

There is an increasing concern in manufacturing systems to reduce electricity costs and lower carbon footprints. There are usually two ways of reducing manufacturing electricity costs. One consists of reducing the total electricity demand by using more efficient equipment and processes, and the other focuses on scheduling algorithms to determine the best response to pricing policies put in place by energy companies. In parts of the United States these pricing policies consist of both a consumption charge, which is related to the total amount of energy consumed, and a demand charge for the highest average power consumed over a given time window. Consideration for both time-dependent consumption charges and demand charges when planning a production schedule (e.g. parallel machine scenario) can reduce the total electricity costs. In this paper, a mathematical optimization model is proposed to schedule jobs in which both demand charges and consumption charges are considered for a facility with parallel machines. Results show that total electricity costs can be reduced significantly when demand charges are included in scheduling decisions versus considering only consumption charges. Results further indicate a significant reduction in peak demands for the facility, which provides a benefit for both the facility and the utility. Results also indicate that considering both demand and consumption charges does not increase the total electricity consumed. This study further analyzes the implications of changing model parameters such as the number of machines, machine utilization, and the price of the demand charge. The results provide a better understanding of how demand charges can affect the schedule of a manufacturing facility if total energy costs are to be minimized.

Flexibility assessment of a combined heat-power system (CHP) with energy storage under real-time energy price market framework

The use of on-site generation can significantly enhance the primary energy savings of a building while acting as a hedge against rising electricity prices. Among other techniques, combined heat and power systems have proved to be reliable and economically suitable technologies in building applications and provision for their promotion has been set out by the European Union over the last few years. Moreover, demand side management programs can provide additional revenue streams which can further benefit the feasibility of combined heat and power technology in a commercial building while providing services to the grid network. In this context, the aim of the present paper is to investigate the techno-economic performance and energy flexibility potential for demand response programs of a gas turbine combined heat and power unit with thermal and electrical storage in commercial buildings. A short assessment overview on demand side management and demand response programs, with a special focus on combined heat and power systems, is provided to introduce the main aspects of these initiatives. Combined heat and power system, technical parameters and operating control strategies are investigated to determine their effects on system efficiencies and economics. Finally, real time pricing and energy storage are examined to establish the implications on a combined heat and power system in a demand-side management framework. Results show that savings of 7% can be obtained by the introduction of thermal energy storage systems, making them suitable for combined heat and power systems in building applications. Operating in a real-time market, the year-on-year savings associated with the combined heat and power are greater than when fixed rate tariffs are considered, especially with the electric energy storage system installed, since it allows the exploitation of price fluctuations. However, the current high investment cost of electric storage makes this solution economically unsuitable, leading to poorer combined heat and power economic performance.

EPC renewable project evaluation: a fuzzy real option pricing model

ABSTRACTEngineer–procure–construct (EPC) projects in the renewable energy field have attracted considerable attention and have become a hot spot of investment because of their management advantages and supportive policies. However, the investment value of EPC projects is difficult to evaluate when considering the uncertainty factors and non-preciseness of input data, which are often neglected by traditional valuation methodology. Therefore, a fuzzy real option pricing model, which can do away with the vagueness of project data, could be a useful approach in evaluating the investment value of EPC projects. Based on the Black–Scholes formula and fuzzy theory, the authors propose a fuzzy real option pricing model that introduces a fuzzy number (T-number) to describe uncertainty factors, and the optimal investment decision criterion is also described to calculate the optimal investment timing. Lastly, a data set of a real EPC project as an illustrative example of valuation is presented, and the results based on the proposed model are satisfactory.

Steering the adoption of battery storage through electricity tariff design

The economic viability of electricity storage using batteries, under different tariff structures and system configurations, is investigated. The economic outcomes of the different combinations of tariff design and system configuration are evaluated. Based on a discussion of the relevant literature, the following tariff designs are used in the study: (i) fixed energy prices, (ii) real-time energy pricing, (iii) fixed rate capacity tariffs, and (iv) capacity dependent capacity tariffs. Next, the different simulated system configurations are outlined: (i) no battery storage, (ii) battery storage only, and (iii) battery storage and decentralized renewable energy production with PV. Our study provides insights for policy makers, showing that capacity block pricing only incentivises storage as part of an (existing) PV installation, while the combination of real time energy pricing and capacity block pricing promotes a wider adoption of battery storage.

Pricing lease agreements incorporating tenant’s downscaling option

PurposeThe purpose of this paper is to analyse the problem that arises when a tenant’s space needs will likely change in the future, but the property owner would prefer to continue renting the initial space to the same tenant. The study builds upon ideas on structuring option values into initial rent and proposes a method for evaluating the value of adaptability for both the tenants and the owners.Design/methodology/approachThe methodology is based on real option pricing, and it includes key variables of building adaptability, lease agreement terms and property market information. The methodology explains the importance of understanding the concept of volatility related to space needs and how it affects the tenant’s decision to either remain or vacate the rented premises. Real option pricing theory highlights the problem of using linearly growing expectations for physical assets and the obvious problems that arise with that assumption.FindingsThis paper suggests that the principles of option pricing could be used in valuing building adaptability to find the optimal initial rent from both the owner’s and the tenant’s perspective. It is pointed that the volatility of the tenant’s future space requirements should drive the effective rent paid by the tenant. The paper argues as to why the owner is better off if the tenant can downscale (with building adaptability) their current space rather than vacate the whole space. Additionally, this paper presents the reasons for why the tenant should pay more for a space that has such a downscaling option. Eventually, both the owner and the tenant are better off because, from the tenant’s perspective, unnecessary relocating costs can be avoided, and from the owner’s perspective, unnecessary re-renting costs can be avoided.Practical implicationsThe paper demonstrates how the downscaling option creates value for both the owner and the tenant. The owner benefits from higher average occupancy rates, and during lease break points, only part of the premises has to be re-rented rather than the entire premises. When these higher occupancy rates are transferred into cash flows with relevant market parameters, it is evident how the rates create extra value for the property owner and for the tenant, subject to lease terms.Originality/valueThe owner benefits from the higher rent, even though there might be more lease break points where parts of the building must be rented out. If these kinds of option values can be communicated transparently, it should be possible for the owner and the tenant to agree on such terms.

Strategic timing of commercial-scale tidal energy investment

The many uncertainties in tidal energy conversion combine to form a significant barrier to raising private-sector capital. Mitigation and management of risk are essential if the industry is to attract equity investors. One way to manage the risk is through investment timing. The option to time an investment has value, which can be estimated. An analysis of an invest-vs-delay decision revealed a persistent, economicallyrational incentive to delay. Further inquiry identified a strategic rationale for delaying investment in tidal energy projects, given the uncertainty still present in the undertaking. As the largest sensitivity in the value of delay is the volatility of the investment’s expected cash flows, an investigation into the prevalent uncertainties was undertaken.
 This paper summarizes the real option valuation model. It then reports on results of a qualitative study of the predominant uncertainties facing developers and conditions that would help move the industry along. Predominant uncertainties reported revolve around technology reliability; site and resource knowledge; prospects for buildout; predictability of government policy and supports; prospects of off-take agreements; and supply chain capacity and costs. These are related back to the variables in the real option pricing model. The model is relevant for companies wishing to systematically evaluate timing options and communicate project value to the investment community. It can also be used by governments to evaluate the design of policies and financial supports in a way that is consistent with the priorities of financial markets.

Effects of Demand Response Siting on High Wind Power Integration in a Transmission Constrained System

Demand Response is now an effective approach to integrate wind power as it could be flexibly deployed to follow the wind power generation. This paper presents a scheduling model with real time pricing (RTP). The effects of wind power location, transmission constraint and DR siting on wind power integration are discussed in this paper. Simulation on PJM-5 bus System indicted that transmission constraint plays a more important role than wind power location on DR distribution and operation cost. It is cost-effective to site wind power at load center and increasing demand elasticity of bus near to congested lines rather than other buses with a limited budget in a transmission constraint system.

Data broadcasting strategies for cognitive radio based AMI networks

Communication systems play an important role in smart grid (SG). Advanced Metering Infrastructure (AMI) is hybrid architecture in smart grid comprising of smart meters and gateways. With AMI network, consumers can achieve demand side management, real time pricing, load scheduling, and upgrading of software updates through gateways to a number of smart meters without the need to visit every place. Information exchange can occur in the form of meter readings from meters to utility, from meters to AMI and from AMI to utility. Broadcasting is one possible solution in these scenarios for information exchange and cognitive radio networks (CRNs) are one possible solution for communication that provides large data transmission utilizing available spectrum resources from licensed and unlicensed bands. Broadcasting is challenging in CRNs due to licensed/primary user (PU) activity, availability of multiple channels, and channel diversity. Therefore, PU protection must be provided at all cost and data transmission should be quick and reliable for a smooth operation. In this paper, we propose two novel reliable schemes, i.e., probability-based broadcasting scheme for CR-based smart grid and area-based broadcasting scheme for CR-based smart grid specifically designed for AMI networks. Our schemes provide reliable data to smart meters with sufficient PU protection. We have compared our schemes with two flooding techniques, i.e., key policy attribute based encryption (Fadlullah et al. in IEEE Commun Mag 50:150–156, 2012) and key management scheme (Liu et al. in IEEE Trans Ind Electron 60(10):4746–4756, 2013). Simulation in NS-2 validates our schemes as a viable solution in cognitive radio-based smart grid systems.

Day Ahead Real Time Pricing and Critical Peak Pricing Based Power Scheduling for Smart Homes with Different Duty Cycles

In this paper, we propose a demand side management (DSM) scheme in the residential area for electricity cost and peak to average ratio (PAR) alleviation with maximum users’ satisfaction. For this purpose, we implement state-of-the-art algorithms: enhanced differential evolution (EDE) and teacher learning-based optimization (TLBO). Furthermore, we propose a hybrid technique (HT) having the best features of both aforementioned algorithms. We consider a system model for single smart home as well as for a community (multiple homes) and each home consists of multiple appliances with different priorities. The priority is assigned (to each appliance) by electricity consumers and then the proposed scheme finds an optimal solution according to the assigned priorities. Day-ahead real time pricing (DA-RTP) and critical peak pricing (CPP) are used for electricity cost calculation. To validate our proposed scheme, simulations are carried out and results show that our proposed scheme efficiently achieves the aforementioned objectives. However, when we perform a comparison with existing schemes, HT outperforms other state-of-the-art schemes (TLBO and EDE) in terms of electricity cost and PAR reduction while minimizing the average waiting time.

Peak and Off-Peak Demand for Electricity: Subsistence Levels and Price Elasticities

In this paper, we explore subsistence levels and price elasticities for residential electricity demand in Sweden. Using a Stone-Geary functional form and unique Swedish data on residential electricity usage, we estimate demand Equations for peak and off-peak demand. We find that the subsistence levels are larger during peak than off-peak, and that there is a substantial variation in these subsistence levels across months. As a result, price responsiveness varies across hours and seasons. This has important policy implications, not the least with respect to effects of real time pricing, as it suggests that there are limits to households’ price responsiveness.

A novel behavioral real time pricing scheme for the active energy consumers’ participation in emerging flexibility markets

Abstract Liberalized electricity markets, smart grids and high penetration of Renewable Energy Sources necessitate the development of novel pricing schemes able to manage energy consumption (energy efficiency services) and harmonize unpredictable/volatile production with ad-hoc consumption (flexibility services). As a result, Energy Service Providers can considerably lower the cost of energy they purchase from the wholesale market and create new revenue streams, while offering consumers lower electricity bills and digital services via online software platforms. Price-based Demand Side Management techniques can trigger the desired behavioral changes and generate novel services and business models for the Energy Service Provider’s participation in congestion, balancing, and other emerging flexibility markets. The energy pricing schemes proposed so far, do not strongly motivate consumers to modify their electricity consumption habits, as they are unfair and thus unable to effectively trigger behavioral changes and offer flexibility services. Based on this observation, we develop a Behavioral Real Time Pricing scheme, which offers an easily adjustable level of financial incentives to consumers, by fairly rewarding the desirable behavioral electricity consumption changes. Performance evaluation results demonstrate that the proposed billing scheme affects the consumers’ behavior much more efficiently than the traditional Real Time Pricing mechanism, outperforming the latter in all widely adopted metrics. Our billing mechanism is able to simultaneously: (i) significantly reduce energy cost compared with Real Time Pricing (10%–30%), (ii) slightly increase consumers’ welfare (2%–4%) and (iii) ensure the fair allocation of financial benefits among the consumers. All these result in significantly increased competitiveness of our billing mechanism in the flexibility markets.

A real time pricing strategy for remote micro-grid with economic emission dispatch and stochastic renewable energy sources

Abstract Micro-grid is a complete energy solution for remote areas facing energy crisis however, energy cost from fuel based sources in a micro-grid is higher than conventional sources. So price-signal based control strategy for the benefit of utility and customers by virtue of renewable integration is analysed. Renewable energy has become less costiler than fuel based sources in a micro-grid. Stochastic power generated from wind turbine (WT) and photo voltaic (PV) along with economic generation from micro-turbine (MT) and fuel cell (FC) is optimised. Anti-predatory particle swarm optimisation (APSO) for non-linear dynamic economic emission dispatch of micro-grid for 24 h with and without renewable energy sources (RESs) is analysed. APSO method with RESs is identified to give better fuel-cost-minimum. Power-to-cost index is proposed as a simple real time pricing (RTP) strategy. The strategy is identified to have the potential to bring down power demand and peak reduction followed by additional reduction in emission and fuel cost in any micro-grid.

The Server Allocation Problem for Session-Based Multiplayer Cloud Gaming

Advances in cloud computing and GPU virtualization are allowing the game industry to move into a cloud gaming era. In this paper, we consider multiplayer cloud gaming (MCG), which is the natural integration of multiplayer online gaming and cloud gaming paradigms. With MCG, a game server and a set of rendering servers for the players need to be located and launched in the clouds for each game session. We formulate an MCG server allocation problem with the objective of minimizing the total server rental and bandwidth cost charged by the cloud to support an MCG session. The MCG server allocation problem is hard to solve optimally. We propose several efficient heuristics to address the problem and carry out theoretical analysis for the proposed hill-climbing algorithm. We conduct extensive experiments using real Internet latency and cloud pricing datasets to evaluate the effectiveness of our proposed algorithms as well as several alternatives. Experimental results show that our best algorithm can achieve near-optimal cost under real-time latency constraints.