RETRACTED ARTICLE: Robust Optimization-Based Energy Pricing and Dispatching Model Using DSM for Smart Grid Aggregators to Tackle Price Uncertainty

Currently, demand response resources can sell load reductions in wholesale energy markets. However, paying for load reductions ultimately results in an unbalanced market, where the amount of resources sold (megawatts) is less than the amount of resources bought (megawatts and “negawatts”). To resolve this imbalance, the ISO must allocate the cost of compensating demand response to those buyers who benefit from reduced LMPs. Current cost allocation methods are quite broad and based on each energy buyer's share of the total load. In an uncongested network, this results in a “fair” allocation of costs, i.e., an allocation proportional to the benefits that each party accrues. However, in a congested network, this is no longer the case, as price separation occurs between nodes. In this paper, we therefore propose a cost allocation method based on LMP sensitivity that accounts for the effect of congestion on the distribution of benefits between nodes with different LMPs. Since this sensitivity-based method only takes into account the cost allocation per node, we also propose a means of allocating costs between individual load serving entities (LSEs) at a single node. Due to this refinement, LSEs are rewarded according to their individual contribution to demand response. Finally, we define a fairness index to evaluate the performance of the proposed method as compared to a load-based allocation. We find that when load reductions are small (1%–3% total load), the fairness index of the proposed method is very close to zero, indicating almost identical benefit to cost ratios for all market participants. Although the fairness index increases with increasing load reductions, results show that even for larger load reductions, the fairness index is still lower for the proposed method than for the load-based allocation method.

Renewable energy resources, notably wind power, are expected to provide considerable portion of the world energy requirements in the near future. Many system operators around the world are challenged by the problems associated with integrating these intermittent resources into the grid. As one of the potential solutions, demand response (DR) is expected to play a major role for mitigating integration issues of intermittent renewable energy resources. In this context, this paper proposes a DR program which helps to integrate wind power by reshaping the load of the system. The DR program provides a framework to procure load reduction from DR resources in the wholesale energy market. The participants in the program submit their offer packages to provide load reduction in the day-ahead energy market. A day-ahead network-constrained market clearing formulation is also proposed which considers the load reduction provided by the DR program participants as an energy market commodity. The proposed method, which is in the mixed-integer linear programming format, determines commitment state of generating units, schedules the energy and spinning reserve provided by generating units, and schedules the load reduction provided by the DR program participants. To reveal the features of the proposed method, several numerical studies are conducted on the IEEE-RTS. The results presented indicate that integrating load reduction in the energy market provides a powerful tool to selectively modify the system load to support wind power integration, while making significant economic and technical benefits for the system.

Demand Response (DR) is an important ingredient of the emerging Smart Grid paradigm. Although much of the DR emerging under Smart Grid is targeted at the distribution level, DR programs are regarded as important elements for reliable and economic operation of the transmission system and the wholesale markets. In the context of Energy and Ancillary Service markets facilitated by the ISOs/RTOs, DR programs may be broadly classified as market-based or reliability-based depending on the conditions under which DR is deployed: The former provide for DR vis-a-vis economic signals, while the latter are generally triggered under emergency conditions. Depending on the ISO/RTO market design and applicable operational standards, the market products DR can offer may include Energy, Reserve Ancillary Services (i.e., Supplemental/Non-spinning Reserve, Spinning/Responsive Reserve, or Regulating Reserve), and Capacity. In this paper we first explore different facets of Demand Response under the Smart Grid paradigm. We then briefly summarize the existing and evolving Demand Response programs at different ISOs/RTOs and the product markets they can participate in. We conclude by addressing some of the challenges and potential solutions for implementation of DR under Smart Grid and Market paradigms.

In the past two decades, interest in demand response (DR) schemes has grown exponentially. The need for DR has been driven by sustainability (environmental and socioeconomic) and cost-efficiency. The main premise of DR is to influence the timing and magnitude of consumption to match energy supply by sharing the benefits with consumers, ultimately aiming to optimize generation cost. As such, the first and primary enabler to DR was the establishment of contemporary electricity markets. Increased proliferation of Distributed Energy Resources (DER) and microgeneration further motivated the participation of consumers as active players in the market, popularizing DR and the wider category of Demand-Side Management (DSM) programs. Smart Grids (SG) have been an enabler to modern DR schemes, with smart metering data providing input to the underlying optimization and forecasting tools. The more recent emergence of the Internet of Energy (IoE), seen as the evolution of SG, is driven by increased Internet of Things (IoT)-enabling and high penetration of scalable and distributed energy resources. In this IoE paradigm being a fully decentralized network of energy prosumers, DR will continue to be a vital aspect of the grid in future Transactive Energy (TE) schemes, aiming for a more user-centered, energy-efficient, cost-saving, energy management approach. This paper investigates original motives and identifies the first mentions of DR in the legislative and scientific literature. Afterwards, the evolution of DR is tracked over the past four decades, attempting to study the co-influence of legislation and research by performing a thorough statistical analysis of research trends on the IEEE Xplore digital library. Finally, conclusions are made as to the current state of DR and future prospects of DR are discussed.

The efficient development of modern distribution system requires the deployment of flexibility services provided by Distributed Energy Resources, like distributed generation, electric energy storage and demand response. This kind of planning tools have to be risk-based, in order to deal with the high level of uncertainties introduced by these new technologies. Suitable models and methodologies for the consideration of the value at risk associated to each choice are essential to compare innovative and conventional planning solutions. In the paper, Demand Response has been modelled with its possible payback effect and the optimal exploitation of this flexibility service with a predefined confidence (residual risk) has been estimated by means of a Robust Linear Programming optimization. The effectiveness of the proposed methodology is demonstrated on a simple distribution network.

The global upsurge in research of smart grid and demand response (DR) is opening up new opportunities and challenges to China's power industry in recent years. The smart grid emphasize the interaction with user particularly, including the information interaction and power interaction, which are achieved through the deployment of various types of DR programs. Meanwhile, the development of smart grid also depends on demand response mechanism in the open market environment. DR coincides with the goal of smart grid which is to achieve a high-security, economical, reliable and efficient power system. So the collaboration between smart grid and demand response is talked in this paper. First,we analyze the relationship between smart grid and demand response systematically.Second,the development conditions of DR project in China and abroad are expounded. Furthermore, the necessity and significance of pushing DR project into smart grid is discussed and summarized, finally, methods and suggestions for achieving the collaboration are presented. (4 pages)

Power systems have been undergoing radical changes in recent years, and their planning and operation will be surely undertaken according to the Smart Grid (SG) vision in the near future. The SG initiatives aim at introducing new technologies and services in power systems, to make the electrical networks more reliable, efficient, secure and environmentally-friendly. In particular, it is expected that communication technologies, computational intelligence and distributed energy sources will be widely used for the whole power system in an integrated fashion. In particular, nowadays, unprecedented challenges like as stringent regulations, environmental concerns, growing demand for high quality, reliable electricity and rising customer expectations are forcing utilities to rethink about electricity generation and delivery from the bottom up. Moreover, the availability of low cost computing and telecommunications technologies, new generation options, and scalable, modular automation systems push utilities to be dynamic, innovative and ambitious enough to take advantage of them. Driven by the dynamics of the new energy environment, leading utilities, technology vendors and government organizations have created a vision of the next generation of energy delivery systems: the Smart Grid. Operational changes of the grid, caused by restructuring of the electric utility industry and electricity storage technology advancements, have created an opportunity for storage systems to provide unique services to the evolving grid. Especially Battery Energy Storage Systems (BESSs), thanks to the large number and variety of services they can provide, are powerful tools for the solution of some challenges that future grids will face. This consideration makes BESSs critical components of the future grids. The BESS can be applied for different services into the different levels of power system chain to satisfy technical challenges and provide financial benefits. In the context of the application of BESSs in SGs, there are two main problems that need to be addressed in a way that exploits the BESS potential, that are linked to their operation and sizing. This thesis focuses on both these aspects, proposing new strategies that allow optimizing the BESS adoption. When dealing with BESSs, sizing and operation are strictly linked. The correct sizing of a BESS, in fact, needs to take into account its operation which in turn will be effected with the aim of optimizing the whole system where it is included. In the first part of this research study, advanced optimal operating strategies were proposed for BESSs by considering both the distribution system operator perspective and the end user. Thus, the proposed operating strategies were performed with the aim of (i) leveling the active power requested by the loads connected to a distribution system (distribution system operator service), (ii) reducing the electricity costs sustained by an end-use costumer that provides demand response (DR) (end user service) and (iii) scheduling a microgrid (µG) with DR resources such as Plug-in Electric Vehicles (PEVs) and Data Centers (DCs) (both the two section service). The proposed strategies also satisfied technical constraints of BESSs and other components of the µG. The second part of the thesis presented the optimal sizing of BESSs aimed at maximizing the benefits related to their use. In the thesis, the sizing, which is performed by considering the end user point of view with reference to both the industrial and residential customers, is effected by adopting both deterministic and probabilistic approaches. With reference to the deterministic approach, a simple and quick closed form procedure for the sizing of BESSs in residential and industrial applications was proposed. In case of probabilistic approach, the case of a BESS installed in an industrial facility was considered and the sizing was performed based on the decision theory. Technical improvements and economic benefits of optimal operation and optimal sizing of BESSs in SG are demonstrated by the obtained results which are reported in the numerical applications. More specifically, it was clearly determined that BESSs can offer technical supports into the distribution operator section of the grid in terms of load management and security challenges. Moreover optimal integration of BESSs into the grid was also appealing for end users thanks to valuable amounts of electricity bill cost reduction. Regarding the original contribution of the thesis, the following considerations can be done. With reference to the load leveling service, an innovative two-step procedure (day-ahead scheduling and very short time predictive control) was proposed which optimally controls a BESS connected to a distribution substation in order to perform load leveling. In case of DR, a proper control of the BESS was proposed in order to perform DR under different price schemes, such as Real Time Pricing (RTP) and Time of Use (TOU) without modifying the daily work cycle of the industrial loads. The control procedure allows achieving contemporaneously two important goals that are the reduction of the bill costs and the prolonging the battery's lifetime so further reducing the costs sustained by the customer. With reference to the scheduling of microgrids, the original contribution of the thesis is focused on the proposal of optimization strategies aimed at managing and coordinating, simultaneously, batteries on board of vehicles or equipping data centers' Uninterruptable Power Supply (UPS) and Distributed Generation (DG) units. Also comparisons among different single-objective based strategies are made in order to highlight the most convenient. With reference to the sizing based on deterministic approach, unlike the other relating literature, the innovative contribution is that the closed form procedure takes into account both the technical constraints of the battery and contractual agreements between the customer and the utility. Moreover, in the economical analysis performed for the sizing, which is applied with reference to both residential and small industrial customers and is based on actual TOU tariffs, a wide sensitivity analysis to consider different perspectives in terms of life span and future costs was performed. Some aspects that affect the profitability of the battery, such as technological limitations (e.g. the battery and converter efficiency), economic barriers (e.g. capital cost and the rate of change of the cost) and variation of the load profile along the years were deeply analyzed. In case of sizing based on probabilistic approach, the original contributions of the thesis are mainly referred to the proposal of a new method that uses a decision theory-based process to obtain the best sizing alternative considering the various uncertainties affecting the sizing procedure. The thesis is organized in three chapters which are dealing with integration of BESSs in SGs. The first chapter reports basic concepts and characteristics of BESSs, fundamental components and features of SGs and different services that BESSs can provide. The optimal operation strategies of BESS are considered in second chapter which includes their problem formulation, solving procedures and results. The third chapter deals with the optimal sizing problem of BESSs for which the problem formulation, solving procedures and results are reported. Finally, the conclusions are presented in the last part of thesis.

In this paper, we consider the problem of distributed load shedding optimization for disaster recovery in smart grids. We develop distributed second-order interior-point based load shedding algorithms that enjoy a fast quadratic convergence rate. Our main contributions are two-fold: (i) We propose a rooted spanning tree based reformulation that enables our distributed algorithm design; (ii) Based on the spanning tree reformulation, we design distributed computation schemes for our proposed second-order interior-point based load shedding. Collectively, these results serve as an important first step in load shedding and disaster recovery that uses second-order distributed techniques.

Demand response and smart grids—A survey

The necessity to seamlessly integrate emerging smart grid (SG) technologies to the electric distribution system has encouraged the development of demand-side management (DSM) and Demand Response (DR) programs. In particular, DR aggregators will play an important role in the new operational paradigm of SG. To propose a new three-phase optimal dispatch model for multiple DR aggregators. The model can consider several DR service providers by means of the explicit inclusion of their characteristics, in the form of cost functions and capacity, in an optimal dispatch model for DR support at the distribution system level. Hence, this proposal helps to eliminate any possible uncertainty about the provision of DR services by improving the traditional hierarchical scheme adopted based on prices issued by the independent system operator. By means of the unbalanced distribution system modeling, DR involvement of single- and three-phase consumers is considered avoiding aggravating the asymmetric balancing between phases, as opposed to traditional positive-sequence DR approaches. The model is implemented and solved combining the two state-of-the-art computational tools, Matlab® and OpenDSS. The former is used to solve the optimization problem, whereas the latter is used to perform numerical simulations of three-phase unbalanced power flow; both tools allow a straightforward model implementation resulting in a tool easily modified and updated. The effectiveness of the proposed approach is numerically demonstrated using the IEEE 13- and 123-node test feeders, in which undesirable operating scenarios are corrected by the implementation of the optimal dispatch of DR resources in very short computational times. Based on the results, it is shown how the ISO is capable to request DR considering several aggregators competing at the distribution system level. Finally, the loss reduction has been included in the objective function, showing that the proposal optimally dispatches the DR aggregators to conveniently minimize the ISO’s operational costs.

Demand response (DR) has become a key feature of the future Smart Grid. This paper assesses the potential of DR in mitigating the redundancy requirement of a high voltage (sub-transmission) grid. DR is considered as a redundancy alternative, activated by network contingencies. The end use appliance consumption pattern along with the load disaggregation technique is applied to determine the achievable DR. The comparison of outage cost for future load is adopted as an assessment method; this comparison is between non-investing in the network, and use of DR as a redundancy alternative. In the presence of DR, novel reliability models are developed in order to find the outage cost. The analysis conducted on a typical Finnish sub-transmission network indicates that the redundant capacity of the network proportional to DR capability can be mitigated. The employment of released redundant capacity for the increasing load will escalate network efficiency. As a consequence investments will be avoided

The vision of smart grids lie in large scale, distributed electrical power systems, equipped with communication and information technologies for automated and sustainable electricity production and distribution. Smart grids require communication with individual energy consumers for enabling energy providers to create a control-feedback loop using time-dependent pricing. Emerging broadband wireless networks hold the key in providing this efficient communication between energy consumers and smart power grids. In this paper we first propose the use of LTE multicast between the aggregator and energy consumers for efficient demand-response management in smart grids. We formulate the multicast scheduling and resource allocation problem for optimal SG-DR programs and provide a greedy solution. Performance analysis and OMNET++ simulation results, using data from Korean power corporation, demonstrate that our proposed LTE multicast and MBSFN solutions are capable of providing low latency (≤ 40ms) and packet drop (≤ 6%), thus resulting in almost 25% shift in peak energy load and up to 30% reduction in energy production cost.

Nowadays, the proportion of the global supply of green energy sources (e.g., solar and wind) is increasing dramatically. Integrating these sources into the current smart grid is very challenging, given that the smart grid has centralized topology which is costly and inefficient. In particular, the current procedures used in the wholesale energy market are slow, time-consuming and thus expensive for small-scale energy sources (e.g., small-scale solar energy farms). Indeed, these sources are excluded in practice from the energy market. Thus, to integrate the increasing number of small-scale energy sources, there is a need to transform the smart grid into a decentralized system. A promising technology to realize this decentralization is Blockchain. With its characteristics like immutability, security, and decentralization, we believe that Blockchain will be the core technology for the future decentralized smart grid. In this paper, we propose a peer-to-peer (P2P) energy trading system based on Ethereum. The objective is to allow any participant (e.g., big/small producers and consumers) to securely trade energy with minimum cost. The proposed approach moves most of the processing and storage off-chain, with the objective to minimize the cost of on-chain (i.e., smart contract) processing and storage while guaranteeing the security of the trading process. Stable coins are used to pay for the trade so as to overcome the problem of the instability regarding cryptocurrency exchange rates. Our proposal has been implemented, tested and deployed on the Ethereum official test network Ropsten. The results show that the proposed approach implements the P2P trading process with very small cost, making it an affordable solution for all participants, including small-scale sources.

Energy management has enhanced sustainability, dependability, and efficiency in smart grids. Urbanisation, technology, and consumer behaviour have boosted need for innovative power use and price control systems. The paper intends to construct ML for smart grid power use and price prediction. This work used an advanced shark smell-tuned flexible support vector machine (ASS-FSVM) to forecast smart grid price and power use. Weather stations, smart meters, and market price databases document power use and pricing. The quality and consistency of data are enhanced via the processes of cleaning and normalizing inputs. PCA reduces dimensionality by extracting pre-processed data characteristics. Optimized and tested FSVM models can anticipate smart grid power use and pricing. ASS may identify the most important dataset properties. The research evaluates electricity consumption forecasting using accuracy (98.05%), recall (98.93%), precision (97.10%), and F1-score (98.04%), and electricity price predicting using MAPE (4.32%), RMSE (5.80%), MSE (8.50%), and MAE (2.95%). The recommended strategy greatly increases forecast accuracy, helping utilities improve grid stability, demand responsiveness, and customer pricing.

The smart grid is widely considered as the advanced version of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the user's energy consumption to reduce the operating expense from expensive generators, and further to defer the capacity addition in the long run. Demand Response functions as a vital controller for the present electrical framework. From outsider see the interest reaction can be viewed as two connections, one related to customer viewpoint and the other is pertinent to energy supplier or market administrators. But both links are driven by the application of different computational techniques. Throughout the smart grid, the advanced computational technique has been properly utilized to enhance the outcome and capability of learning. The salient feature of this paper is to criticize the applicability of computational techniques for demand response. The paper explores major aspects such as DR issues, different computational techniques, and future extensions of demand response.