This paper presents a comprehensive framework for the assessment of reliability and risk implications of post-fault Demand Response (DR) to provide capacity release in smart distribution networks. A direct load control (DLC) scheme is presented to efficiently disconnect DR customers with differentiated reliability levels. The cost of interrupted load is used as a proxy for the value of the differentiated reliability contracts for different customers to prioritize the disconnections. The framework tackles current distribution system operator (DSO)’s corrective actions such as network reconfiguration, emergency ratings and load shedding, also considering the physical payback effects from the DR customers’ reconnection. Sequential Monte Carlo simulation (SMCS) is used to quantify the risk borne by the DSO if contracting fewer DR customers than required by deterministic security standards. Numerical results demonstrate the benefits of the proposed DR scheme, when compared to the current DLC scheme applied from the local DSO. In addition, as a key point to boost the commercial implementation of such DR schemes, the results show how the required DR volume could be much lower than initially estimated when properly accounting for the actual risk of interruptions and for the possibility of deploying the asset emergency ratings. The findings of this work support the rationale of moving from the current prescriptive deterministic security standards to a probabilistic reliability assessment and planning approach applied to smart distribution networks, which also involves distributed energy resources such as post-contingency DR for network support.
Spyros Giannelos, Ioannis Konstantelos, Goran Štrbac
Under the current EU legislation, Distribution Network Operators (DNOs) are expected to provide firm connections to new DG, whose penetration is set to increase worldwide creating the need for significant investments to enhance network capacity. However, the uncertainty around the magnitude, location and timing of future DG capacity renders planners unable to accurately determine in advance where network violations may occur. Hence, conventional network reinforcements run the risk of asset stranding, leading to increased integration costs. A novel stochastic planning model is proposed that includes generalized formulations for investment in conventional and smart grid assets such as Demand-Side Response (DSR), Coordinated Voltage Control (CVC) and Soft Open Point (SOP) allowing the quantification of their option value. We also show that deterministic planning approaches may underestimate or completely ignore smart technologies.
Installation of smart meters is increasing world-wide, opening the possibility to implement time-of-use (TOU) tariffs to moderate peak loads. This study is focused on the design of an optimal opt-in residential TOU tariff. Residential consumers are assumed to act in their private interests and maximize utility in response to tariffs. The regulated utility has a broader interest in maximizing societal welfare. However, the regulated utility cannot impose household behavior and cannot directly observe household type. Instead, the regulated utility must offer contract options including both a TOU tariff and current pricing to allow households to self-select the plan best suited to their interests. This paper proposes a simple flexible household utility function that can be calibrated with minimal data to describe diverse household behaviors and reveal household responses to different prices. An optimal pricing model is designed for the regulated utility, taking account of asymmetric information and potential household opportunistic behavior. Using economic constructs from principal-agent theory, the pricing model ensures participation among households most aligned with the regulated utility's desire. The pricing model can also be extended for competitive markets. A case study is performed to demonstrate the benefit the optional TOU tariff can realize.
Large-scale deployment of reliable smart electricity metering networks has been considered as the first step towards a smart, integrated and efficient grid. On the consumer’s side, however, the real impact is still uncertain and limited. This paper evaluates the consumer’s perspective in the city of Västerås, Sweden, where full implementation of smart meters has been reached. New services, such as consumption feedback and the possibility to choose dynamic electricity pricing contracts, have been available from the adoption of this infrastructure. A web-based survey evaluating customers’ perception of these new services was carried out. The survey included consumers’ personal information, preferences about the type of information and the frequency of delivery and the preference for electricity pricing contracts. The results showed that the electricity consumption information offered by distribution system operators (DSOs) today is not detailed enough for customers to react accordingly. Additionally, while variable pricing contracts are becoming more popular, the available pricing schemes do not encourage customers to increase their consumption flexibility. Therefore, more detailed information from the smart meters should be made available, including disaggregated electricity consumption per appliance that would allow consumers to have more control over their energy consumption activities.
With the grasp of a smart grid in sight, discussions have shifted the focus of system security measures away from generation capacity; apart from modifying the supply side, demand may also be exploited to keep the system in balance. Specifically, Demand Response (DR) is the concept of consumer load modification as a result of price signaling, generation adequacy, or state of grid reliability. Implementation of DR mechanisms is one of the solutions being investigated to improve the efficiency of electricity markets and to maintain system-wide stability. In a liberalized electricity sector, with a smart grid vision that is committed to market-based operation, end-users have now become the focal point of decision-making at every stage of the process in producing, delivering and consuming electricity. DR program implementation falls within the smart grid domain: a complex socio-technical energy system with a multiplicity of physical, economic, political and social interactions. This thesis thus employs both qualitative and quantitative research methods in order to address the ways in which residential end-users can become active DR flexibility providers in deregulated European electricity markets. The research focuses on economic incentives including dynamic pricing contracts, dynamic distribution price signals and the aggregation of load flexibility for participation in the various short-term electricity markets.
The smart grid is widely considered as an efficient and intelligent power system. With the aid of communication technologies, the smart grid can enhance the efficiency and reliability of the grid system through intelligent energy management. However, with the development of new energy sources, storage and transmission technologies together with the heterogeneous architecture of the grid network, several new features have been incorporated into the smart grid. These features make the energy trading more complex and pose a significant challenge on designing efficient trading schemes. Based on this motivation, in this paper, we present a comprehensive review of several typical economic incentive approaches adopted in the energy-trading control mechanisms. We focus on the technologies that address the challenges specific to the new features of the smart grid. Furthermore, we investigate the energy trading in a new cloud-based vehicle-to-vehicle energy exchange scenario. We propose an optimal contract-based electricity trading scheme, which efficiently increases the generated profit.
Although researchers have acknowledged the issue of commercial viability previously, it is only recently that they have laid emphasis on addressing the relative importance of commercial viability to catalyze the dissemination of decentralized sustainable energy systems to rural consumers in developing countries. A business enterprise is said to be commercially viable if its revenues are > costs. Here in this thesis these business enterprises or promoters of efforts are called as renewable energy companies (REC’s). Moreover researchers have failed to acknowledge or address the role of revenues even after acknowledging the merits of a market driven approach as opposed to donor driven approach. Given such a high relevance of revenues in a market approach to operate successfully and a lack of focus on the same by researchers, in this thesis we will analyze the practical issue of commercial viability of Indian REC’s through the lens of revenue model, while also addressing the literature gap on revenue drivers or revenue model components by exploring various relevant revenue drivers of commercially viable REC’s. This study takes an exploratory case study approach to enlist all the relevant revenue driver or revenue model components that are relevant for REC’s to attain commercial viability. This thesis primarily consists of three subsequent phases: first phase: theoretical gap identification. Second phase: identification of types and components of a revenue model and third phase: building a revenue driver – commercial viability framework. The aim of the first phase was to narrow in on the literate gap and also present relevant background literature. The first phase yielded the literature gap on revenue model components in addressing the practical issue of commercial viability. The aim of the second phase was to identify revenue model types and components. The result of which was that two types of revenue models namely: ownership and service revenue models was discovered. Most importantly six potential revenue drivers were also discovered. They are: consumer trust, pricing strategy, willingness to pay, flexibility of payments, number of users and revenue sharing. These six revenue drivers were derived on the premise that they would increase revenues such that REC’s attain commercial viability. This made up our initial conceptual model. Next, the aim of the third phase was to build a framework on revenue drivers or revenue model components – commercial viability of Indian REC’s. In order to do so firstly we analyzed cases where the initial conceptual model is leveraged into a more relevant context of Indian REC’s. The case studies were based on SIMPA Networks, Onergy, Rural Spark and MeraGao Power (MGP). All of these cases primarily are Indian companies exclusively catering to the Indian rural market otherwise also known as REC’s or Indian REC’s. The results of this section yielded us a relevant set of 12 revenue drivers i.e. six more in comparison to the initial set of 6 revenue drivers. They are consumer trust, supplier trust, pricing strategies, willingness to pay, flexibility of payments, number of users, revenue sharing, consumer financing, size of payments, service customization, after sales service/maintenance and discounts. Secondly, a cross case analysis was performed wherein findings from each case are pitched against each other to find the similarities and differences. The result of this section was firstly that, any sort of generalizations based on the type of revenue models was hard to come by and most importantly the type of revenue model only signified its affect on the source of financing and could play no role in explaining how and why commercial viability was being achieved. Moreover it also led to an inference that service revenue model poses more risk than ownership revenue model but however commercial viability was achieved by adopting both types of revenue model, which was quite the contrary to the outcome of literature survey. Secondly, list of revenue drivers was further narrowed to 10 from the previous list of 12. Basically willingness to pay was eliminated because it was already being considered in pricing strategies and number of users was also removed because it affected the commercial viability of REC’s in terms of both costs and revenues whereas the others only impacted only revenues. The final set of relevant revenue drivers are: consumer trust, supplier trust, pricing strategies, flexibility of payments, size of payments, revenue sharing, consumer financing, service customization, after sales service/maintenance and discounts. Lastly, a set of three factors was identified that actually contributed to the increase in revenues such that revenues were > costs. Or in other words served as a link between revenue drivers and commercial viability. They are namely: rate of adoption, recoupment of costs (regular payments) and retention. It is these afore mentioned revenue drivers that impact the three factors, which subsequently drive or increase revenues such that commercial viability can be attained. The ownership revenue model primarily derives its revenues from only the adoption factor, which subsequently brings in revenues to attain commercial viability. That said the adoption of DSE’s by the rural consumers is contingent or dependent on revenue drivers like consumer financing and size of payments among others. The revenues of REC’s employing service revenue model primarily depended on all the three factors like rate of adoption, recoupment of costs and retention. More specifically the revenue drivers should be conducive to rural customers such that they firstly adopt the product and/or service and most importantly make regular payments, which translates to revenues while retaining the existing customers. Moreover the retention factor only applies to REC’s that adopt a service revenue model with only a service platform like MGP unlike other REC’s, which adopt a only a product platform like Onergy or both product and service platform like in the case of SIMPA and Rural spark. In the backdrop of afore mentioned scientific implications several managerial implications can also be derived. Among many the key take away for incumbent managers and future potential entrants will be to look at each of the revenue drivers and adopt them carefully such that commercial viability can be attained contingent on the his/her appetite for risk and most of all focus less on the type of revenue model because that is not going to help achieve commercial viability. Future research should be aimed at firstly developing a more elaborate revenue driver- commercial viability framework. After which each of the revenue driver’s true affects on each of the factors should be quantitatively determined. This further helps to gain greater generalizability. That said the key limitation of this thesis is that it focuses only on one country i.e. India among other developing countries.
Demand Response (DR) is considered a promising approach to cope with the increasing variability in power grids due to the penetration of renewable energy sources. However, it still remains a challenge to manage the aggregation of a large number of heterogeneous loads to achieve a desired response, especially at a fast time scale. In this paper, we present a system-level modeling and control design approach for DR management in smart grids, by following a contract-based methodology. Given a set of flexible DR loads, capable of adapting their power consumption upon external requests, we find a strategy for an aggregator to optimally track a DR requirement by combining the individual contributions of the DR components. In our framework, both the design requirements and the DR components' interfaces are specified by assume-guarantee contracts expressed using mixed-integer linear constraints. Contracts are used to formulate an optimal control problem, which is solved repeatedly over time, in a receding horizon fashion. We illustrate the effectiveness of our methodology for a set of DR components including fans and pumps, showing that it enables modular development of non-disruptive DR control schemes.
Marilena Minou, George D. Stamoulis, George Thanos, Vikas Chandan
Demand Response (DR) programs constitute an efficient way to alleviate the problem of peak demand in smart grids. While the potential impact of DR can be significant, its success essentially depends on the participation and responsiveness of consumers. In this paper, we focus on the design of effective contract-based automated DR programs by energy providers that own supportive generators to meet excess demand and employ DR as a means to avoid their costly activation. We derive a theoretically justified formula for the amount of incentives that should be offered to a consumer to accept such a contract. Based on this, we introduce an algorithm for selecting the optimal set of consumers in terms of total incentives. This algorithm is employed under two different policies for restricting (in a different way) the discomfort caused to consumers. We evaluate these policies using real-world data and present interesting insights about the efficient selection of consumers to be targeted for DR and the total amount of incentives offered to them by the provider.
In the smart grid, large consumers can procure electricity energy from various power sources to meet their load demands. To maximize its profit, each large consumer needs to decide their energy procurement strategy under risks such as price fluctuations from the spot market and power quality issues. In this paper, an electric energy procurement decision-making model is studied for large consumers who can obtain their electric energy from the spot market, generation companies under bilateral contracts, the options market and self-production facilities in the smart grid. Considering the effect of unqualified electric energy, the profit model of large consumers is formulated. In order to measure the risks from the price fluctuations and power quality, the expected utility and entropy is employed. Consequently, the expected utility and entropy decision-making model is presented, which helps large consumers to minimize their expected profit of electricity procurement while properly limiting the volatility of this cost. Finally, a case study verifies the feasibility and effectiveness of the proposed model.
This paper proposes an efficient method for designing a contract model of the weather derivatives between energy utilities in Smart Grid. It is well-known that the weather conditions bring a profit decline or the increase of expenses to do damage to sound management. Weather derivatives are useful for solving such a problem. One of the ideas is to use the complementary relationship between electric power and gas companies in a sense that electric power companies are apt to make profits in hot summer while gas companies are inclined to reduce revenue. This paper focuses on how to create a reasonable contract model of the weather derivative. In this paper, EPSO (Evolutionary Particle Swarm Optimization) of meta-heuristics is applied to designing a contract model of the weather derivative. The proposed method aims at equalizing the mean and the variance of the payoffs between the power and gas companies. To enhance the model accuracy, DA clustering of global clustering is used to classify the historical data into clusters. The effectiveness of the proposed method is demonstrated for the real data in Tokyo, Japan.
Decentralized generation on the owners premises can be used on-site which we call auto-consumption. In a system with energy based (volumetric) network charges this reduces network income and in turn causes higher network charges on remaining usage with redistributional effects among customers. We estimate the effect from PV auto-consumption in Germany to be small in many network areas but significant in others leading to significant regional unequality. The effect may be self-enforcing and could increase strongly with pronounced shares of PV and auto-consumption. Further analysis should address the causal links to inform modifications to network charging for higher cost causality in power systems with high shares of (renewable) decentralized generation. Meanwhile a higher fixed component in network charges could be a pragmatic way to mitigate effects and serve a more equal participation of auto-consumers in network financing.
The editorial board announced this article has been retracted on July 19, 2016. If you have any further question, please contact us at: cis@ccsenet.org
Samira Rahnama, Jan Dimon Bendtsen, Jakob Stoustrup, Henrik Rasmussen
Exploitation of flexible consumption in the future smart grid requires new actors and infrastructure. In this paper, we propose a hierarchical setup in which a central controller, a so-called “aggregator,” is responsible for managing the flexibilities of industrial thermal loads via a contract-based direct control policy. The aggregator manipulates the consumption profile in an optimal and robust manner in order to provide upward and downward regulating power services. To this end, we consider a robust model predictive control design at the aggregator. The performance of the proposed controller is evaluated by simulating specific case studies involving a supermarket refrigeration system and a heating, ventilation, and air conditioning chiller in conjunction with an ice storage. In addition, we provide a comparison between heterogeneous and homogeneous aggregation of different thermal loads through simulation examples.
India today needs to have around 2,OO,OOO MW of power to meet its current energy needs, but it is able to provide only about two third of it. Additional finances are difficult to come by and the infrastructure is often not available to make it reach the remote areas. Heavy roistering in city areas has been a perpetual feature in some states. Many of the dwellers of small cities have come to depend on what is called an 'inverter' and its associated battery based storage system to cope up with the frequent power outages. Seen from another perspective these systems only lack the solar panels to become completely self contained power systems. Thus addition of solar panels to these will be only an incremental cost. By encouraging this approach several problems can be solved all the same time. First of all this additional investment will be from the users themselves. Next the power generated will be environmentally friendly and the regular power supply and the grid may need to be used sparingly. These storage based systems may become parts of Smart Grids for the future as they may be further evolved to feed power into the grid.Evaluation of the performance of these systems has been studied through simulation and the economics of the system has been investigated under various conditions for typical users. The proposed system has been compared with the early telecom systems in India that were based on land lines and could not be expanded fast enough. Later the privatized and decentralized wireless based approach provided the desirable solutions.
In order to increase the utilization of renewable energy sources and to reduce the need of generator-provided ancillary services and inefficient peaking generation, buildings are progressively transforming into smarter electricity consumers and active participants in power system operations. In this work, an R-C thermodynamic model of building that strongly relates to the power consumption of heating ventilation air-conditioning system (HVAC) is firstly introduced. To offer the aggregated flexible HVAC power to the grid as regulating power, we propose a building-aggregator-grid contract framework and formulate a robust model predictive control (MPC) algorithm which both maximizes the profit of aggregator and minimizes the payment of each participating building to optimally declare power flexibility. Lastly, simulation results on a group of real and hypothetical buildings validate the feasibility of the proposed method.
Particularly with the rise of intermittent renewable energy sources, balancing electricity consumption and production is the core challenge of smart grids. The most important drawback of existing approaches is that controlling loads or producers is typically heavily regulated by contracts or laws. These real-world constraints significantly increase the complexity associated with achieving the aforementioned balance. In this paper, we propose a planning algorithm for smart grids that respects contractual or legal constraints while balancing power consumption and production. Additionally, our planning approach manages energy producers as well as consumers. Thus, it achieves the balance in the grid by controlling a combination of producers and consumers.
Vivek Kuthanazhi, Anil Kottantharayil, N. C. Narayanan
Decentralization of energy planning through local self-governments has become a possibility since the development of solar photovoltaics and other small scale power generation technologies. The state of Kerala, India is well known for its success stories in decentralized administration of villages through panchayati raj1institutions. Panchayats in Kerala have proved their efficiency in the past by handling development projects, hence the scope for implementing decentralized PV power plants in a panchayat scale in Kerala is quite high. A study was conducted at Chendamangalam gram panchayat, Kerala, India to assess the electricity demand and rooftop PV power generation potential of a typical panchayat located in the low-lands of Kerala. The changing trends in electricity consumption due to the use and spread of new appliances and 100% grid extension to the villages could be discerned. Appliances like televisions, ceiling fans, water pumps, washing machines, electric mixers, refrigerators etc has penetrated well in this area. Inverter based power back up systems and new high power equipments like induction cookers, air conditioners, water heaters etc. are increasingly being used by the domestic consumers, which indicate a possible leap in electricity demand in the near future. The study could conclude that the collective shade free rooftop area from nearly 6500 buildings in the panchayat can support nearly 11.3 MW rooftop PV installations and it is more than enough to meet the current energy demands of the panchayat. Innovative design of custom PV solutions and institutionalization of operation and maintenance, supported with clear government policies and finance options can help in transforming a panchayat into an energy surplus entity.