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The aggregation of distributed energy resources (DERs) draws much attention since their penetration increases continuously. We derive the mathematical formulation of the exact aggregate feasible region (AFR) of multiple DERs. The derivation is based on analyzing the redundancy of all possible constraints in the Fourier-Motzkin Elimination (FME) process. In the exact AFR model, the number of constraints is regardless of the number of DERs but exponential with the number of time intervals. Although the computational complexity of the exact AFR is dramatically simplified compared with the original FME, there are still too many constraints for the exact AFR model to be applied in practice. Hence, we propose the $k$ th-order approximate models and two types of multi-timescale approximate models. Illustrative cases show the necessity of each constraint in the proposed models compared with the aggregated power and energy boundary model. Numerical simulations verify the accuracy of the exact AFR model. Besides, the second-order approximate model performs best considering the balance between accuracy, economics, and computational complexity. [ABSTRACT FROM AUTHOR]

This paper proposes a semi-analytical method to obtain the equilibrium bidding strategies for generation and demand units in a combined oligopoly and oligopsony wholesale electricity market. Such market structure is the outcome of the increasing deployment of demand response programs that facilitate active participation of demand-side players in the price-setting process. In this analysis, the concept of supply function equilibrium (SFE) is used to investigate the oligopolistic competition among generation units. The SFE model is extended and the demand function equilibrium (DFE) is obtained to study the oligopsonistic competition among demand units. The economic behavior of a market participant is formulated as a bi-level programming (BLP) problem. The imperfect competition among generation units, as well as among demand units, are modeled as a non-cooperative game. Next, a direct method is developed to calculate all candidate equilibriums of the market, and the locational marginal prices (LMPs) in terms of the bidding strategies of the market participants. The BLP problem is solved by obtaining the coordinated Pareto-dominant Nash equilibrium of the market participants’ non-cooperative games. Finally, the proposed analysis is examined in case studies. Accordingly, we report insightful observations with respect to the impact of the changes in the new market structure, at firm-level and market-level, such as in terms of mitigating market power of generation units, the market clearing prices and quantities, surplus for generation units and demand units, and potential impact on market efficiency. [ABSTRACT FROM AUTHOR]

Towards the development and demonstration of an innovative business model where the value proposition for consumers/prosumers, aggregators and network operators are well maintained, this study assesses the performance of different aggregation control strategies for a distributed energy storage based residential virtual power plant (VPP). A special focus is given on the social welfare and network strength and their relation to energy storage capacity and power budgets allocation. The study is based on a real-life demonstration project, StoreNet where the basic self-consumption (SB-SC) control strategy has already been deployed. Analysing one-year measured data, it is observed that the implemented SB-SC approach allows 16%−19% electricity cost-saving, whereas the proposed VPP-bill minimisation approach can benefit from 37%−42% cost saving. This is also 7%−8% higher than the single home bill minimisation approach where the community does not participate in the VPP model. In contrast, the peak shaving approach is more favourable for the network operator. It reduces the load peak by 46.5%−64.7% and also drastically reduces the benefits for the customers and aggregator. Based on these studies and learning, some recommendations are made addressing the integration aspect of residential VPP and the future development of this concept for the local and wholesale energy markets. [ABSTRACT FROM AUTHOR]

This paper presents a distribution system operator (DSO) framework for wholesale and retail market participation of distributed energy resources (DERs) aggregators. The DSO coordinates aggregators’ energy and regulation offers as well as end-users’ energy consumption through the unbalanced retail market and submits balanced energy and regulation offers to the wholesale market on behalf of all the aggregators and end-users within its territory. Various kinds of DER aggregators including demand response aggregators (DRAGs), energy storage aggregators (ESAGs), electric vehicle (EV) charging stations (EVCSs), dispatchable distributed generation aggregators (DDGAGs), and renewable energy aggregators (REAGs) are modeled. To handle unbalanced distribution grids with single-phase aggregators, a linearized unbalanced power flow is tailored to model operating constraints of the distribution grid with various aggregators. A market settlement approach is proposed for the DSO, which coordinates with wholesale market clearing process and ensures the DSO’s non-profit characteristic. It is proved that at the wholesale-DSO coupling substation, the total payment received/compensated by the DSO under the wholesale price is identical to that under three single-phase retail prices for each phase at the substation. Case studies are performed on the modified IEEE 33-node and 240-node distribution test systems to investigate the market outcomes of the proposed DSO. [ABSTRACT FROM AUTHOR]

Virtual power plants (VPPs) have emerged as a way to coordinate and control the growing number of distributed energy resources (DERs) within power systems. Typically, VPP models have focused on financial or commercial outcomes and have not considered the technical constraints of the distribution system. The objective of this article is the development of a technical VPP (TVPP) operational model to optimize the scheduling of a diverse set of DERs operating in a day-ahead energy market, considering grid management constraints. The effects on network congestion, voltage profiles, and power losses are presented and analyzed. In addition, the thermal comfort of the consumers is considered and the tradeoffs between comfort, cost, and technical constraints are presented. The model quantifies and allocates the benefits of the DER operation to the owners in a fair and efficient manner using the Vickrey–Clarke–Grove mechanism. This article develops a stochastic mixed-integer linear programming model and various case studies are thoroughly examined on the IEEE 119 bus test system. Results indicate that electric vehicles provide the largest marginal contribution to the TVPP, closely followed by solar photovoltaic (PV) units. Also, the results show that the operations of the TVPP improve financial metrics and increase consumer engagement while improving numerous technical operational metrics. The proposed TVPP model is shown to improve the ability of the system to incorporate DERs, including those from commercial buildings. [ABSTRACT FROM AUTHOR]

An aggregation of distributed energy resources (DERs) can bring economic and technical benefits for the DER owners and system operator. However, the operation of DERs encounters various uncertainties, which can seriously impact the benefits of DER aggregation. This article presents a new operation optimization approach for an aggregator of DERs considering the unavailability of DERs (as discrete uncertainty sources) as well as forecast uncertainties of electricity prices, solar powers, and wind powers (as continuous uncertainty sources). The proposed approach for DER aggregator (DERA) operation optimization comprises stochastic multiobjective information-gap decision theory (IGDT) to model these discrete and continuous uncertain variables. Moreover, a hybrid endogenous/exogenous scenario generation method is incorporated into the proposed approach to enhance the efficiency of the stochastic programming part by producing decision-dependent scenario trees. The proposed approach is formulated as a nested bilevel optimization model. The proposed approach is compared with other DERA operation optimization models using an out-of-sample analysis method. The comparative results illustrate the superiority of the proposed stochastic multiobjective IGDT approach over various deterministic, stochastic, and IGDT methods. In addition, the high tractability of the proposed solution method is illustrated, while its linearization error for the stochastic multiobjective IGDT problem is well below 1%. [ABSTRACT FROM AUTHOR]

Restructuring and privatization in power systems have resulted in a fundamental transition of conventional distribution systems into modern multi-agent systems. In these structures, each agent of the distribution system would independently operate its local resources. In this regard, uncertainties associated with load demands and renewable energy sources could challenge the operational scheduling conducted by each agent. Therefore, this paper aims to develop a distributed operational management for multi-agent distribution systems taking into account the uncertainties of each agent. The developed framework relies on alternating direction method of multipliers (ADMM) to coordinate the operational scheduling of the agents in a distributed manner. Moreover, a robust optimization technique is employed to consider the worst-case realization associated with the operation of each agent. Finally, the proposed framework is implemented on IEEE 37-bus network to analyze its efficacy in distributed robust operational management of distribution systems with multi-agent structures. [ABSTRACT FROM AUTHOR]

In this paper we consider the aggregation of common convex and non-convex individual Demand Response (DR) models for responsive loads, and apply the Shapley-Folkman (SF) lemma to show that such an aggregate is approximately convex in its action space and cost, and strictly convex under mild conditions. We then discuss how reduced order convex aggregate models can be passed on to System Operators for inclusion in economic and operational models, including a novel polytope approximation as well as an ellipsoidal model derived in a distributed fashion. Numerical simulations confirm our application of the Shapley-Folkman lemma and show that the new reduced order models outperform conventional virtual generator approximations. [ABSTRACT FROM AUTHOR]

Increasing the penetration of renewables on prosumers' side brings about operational challenges in the distribution grid due to their variable and uncertain behavior. In fact, these resources have increased the distribution grid net load fluctuation during recent years. In this article, the flexibility-oriented stochastic scheduling of a microgrid is suggested to capture the net load variability at the distribution grid level. In this scheduling, the flexibility limits are set to manage the net load fluctuation at a desirable level for the main grid operator. The uncertainties of load and renewables are considered, and their uncertainties are under control by the risk-averse strategy. Moreover, multiperiod islanding constraints are added to the problem, preparing the microgrid for a resilient response to disturbances. The model is examined on a typical distribution feeder consisting of prosumers and a microgrid. The numerical results are compared for both flexibility-oriented and traditional scheduling of a microgrid at the distribution level. The proposed model reduces the net load ramping of the distribution grid using an efficient dispatch of resources in the microgrid. A sensitivity analysis is also carried out to show the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Risk preference is an important factor in electricity market strategy analysis and decision-making. The existing methods of risk preference analysis need to design and execute questionnaires or experiments on the subjects, and hence are costly and time-consuming for bidding in electricity markets. This article proposes a new method of data-driven risk preference analysis for power generation plants based on historical data and inverse reinforcement learning. Historical data are transformed to the transition function model according to the specific market mechanism. An adjusted inverse reinforcement learning model is thereafter proposed along with the optimization objective and technical constraints. The proposed method is tested in a simulated electricity market environment using the Australian Energy Market Operator (AEMO) day-ahead bidding data. Simulation results show that 1) thermal power plants prefer to adjust risk preferences within the day; 2) apart from the thermal power plants, the rest types of power plants are risk-neutral; 3) the daily risk preference trend of the thermal power plants varies in different seasons and is closely related to the load level. [ABSTRACT FROM AUTHOR]

To accomplish feasible large-scale integration of distributed energy resources (DER) into the existing grid system, microgrid implementation has proven to be the most effective. This article reviews the vital aspects of DER based microgrid and presents simulations to investigate the impacts of DER sources, electric vehicles (EV), and energy storage system (ESS) on practicable architectures’ resilient operation. The focus is primarily on the concept and definition of microgrid, comparison of control strategies (primary, secondary, and tertiary strategies), energy management strategies, power quality (PQ) issues associated with DER based microgrid, and state-of-the-art entities such as ESS and EV's applications toward microgrid reliability. Following discussion on the different attributes of DER sources-based microgrid, simulations are performed to verify the results of the past works on the effects of solar, wind energy sources, ESS, and EVs on the microgrid frequency response. Additional simulations are conducted to assess the influences of DERs, ESS, EVs, and their operational strategies on the microgrid reliability aspects. [ABSTRACT FROM AUTHOR]

Coordinated operation of various energy sources has drawn the attention of many power producers worldwide. In this paper, a Concentrating Solar Power Plant (CSPP) along with a wind power station, a Compressed Air Energy Storage (CAES) unit, and a Demand Response Provider (DRP) constitute the considered Hybrid Power Producer (HPP). In this regard, this paper deals with the optimal participation of the mentioned HPP in the Day-Ahead (DA), and intraday electricity markets by benefiting from the joint configuration of all accessible resources. To attain risk-averse strategies in the suggested model, Conditional Value-at-Risk (CVaR) based on the ε-constraint technique is employed, while its efficiency is validated compared to the previously applied method to such problems. On the whole, the main contributions of this work lie in: 1) proposing a novel model for optimal behavior of a CSPP-based HPP in DA, and intraday markets using a three-stage decision-making architecture, and 2) developing a bi-objective optimization framework to improve the functioning of the risk-constrained algorithm. Simulation results reveal that taking advantage of the CSPP in the intraday market, and coordinated operation of all resources not only enhance the profitability of the system but also lessen the associated risk compared to the previous models. [ABSTRACT FROM AUTHOR]

The recent advances regarding the decentralization of renewable energy production, the new technologies involved in the management of smart grids, and the opening of national energy markets, enriched with the use of demand-response strategies, have led to a notable diffusion of local energy markets. A local energy market is defined as an aggregation of energy producers, consumers, and prosumers that are located in a restricted area and see an interest in joining together to form a so-called “energy district.” In this paper, we present a two-stage approach that enables sharing renewable energy within a district and minimizes the costs and/or maximizes the revenues deriving from the provision and the sale of energy, both for single prosumers and for the district as a whole. The main novelty with respect to the state-of-the-art is the introduction in the optimization process of a second stage that, starting from the energy exchanges determined in the first stage, redistributes to the prosumers the surplus energy, i.e., the energy produced locally that exceeds the demand of the prosumers. The two-stage approach benefits have been assessed in a real-life testbed deployed on an Italian university campus. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESSs) are the key to overcoming challenges to achieve the distributed smart energy paradigm and zero-emissions transportation systems. However, the strict requirements are difficult to meet, and in many cases, the best solution is to use a hybrid ESS (HESS), which involves two or more ESS technologies. In this article, a brief overview of the HESS, highlighting its advantages for a wide range of applications, is addressed. [ABSTRACT FROM AUTHOR]

The need to limit climate change has led to policies that aim for the reduction of greenhouse gas emissions. Often, a trade-off exists between reducing emissions and associated costs. In this article, a multi-objective optimization framework is proposed to determine this trade-off when operating a Community Energy Storage (CES) system in a neighbourhood with high shares of photovoltaic (PV) electricity generation capacity. The Pareto frontier of costs and emissions objectives is established when the CES system would operate on the day-ahead spot market. The emission profile is constructed based on the marginal emissions. Results show that costs and emissions can simultaneously be decreased for a range of solutions compared to reference scenarios with no battery or a battery only focused on increasing self-consumption, for very attractive CO2 abatement costs and without hampering self-consumption of PV-generated electricity. Results are robust for battery degradation, whereas battery efficiency is found to be an important determining factor for simultaneously decreasing costs and emissions. The operational schedules are tested against violating transformer, line and voltage limits through a load flow analysis. The proposed framework can be extended to employ a wide range of objectives and/or location-specific circumstances. [ABSTRACT FROM AUTHOR]

A regret-based stochastic bi-level framework for optimal decision making of a demand response (DR) aggregator to purchase energy from short term electricity market and wind generation units is proposed. Based on this model, the aggregator offers selling prices to the customers, aiming to maximize its expected profit in a competitive market. The clients’ reactions to the offering prices of aggregators and competition among rival aggregators are explicitly considered in the proposed model. Different sources of uncertainty impressing the decisions made by the aggregator are characterized via a set of scenarios and are accounted for by using stochastic programming. Conditional value-at-risk (CVaR) is used for minimizing the expected value of regret over a set of worst scenarios whose collective probability is lower than a limitation value. Simulations are carried out to compare CVaR-based approach with value-at-risk (VaR) concept and traditional scenario based stochastic programming (SBSP) strategy. The findings show that the proposed CVaR strategy outperforms the SBSP approach in terms of making more risk-averse energy biddings and attracting more customers in the competitive market. The results show that although the aggregator offers the same prices in both CVaR and VaR approaches, the average of regret is lower in the VaR approach. [ABSTRACT FROM AUTHOR]

The penetration of distributed energy resources (DER), including distributed generators, storage devices, and demand response (DR) is growing worldwide, encouraged by environmental policies and decreasing costs. To enable DER local integration, new energy players as aggregators appeared in the electricity markets. This player, acting toward the grid as one entity, can offer new services to the electricity market and the system operator by aggregating flexible DER involving both DR and generation resources. In this paper, an optimization model is provided for participation of a DER aggregator in the day-ahead market in the presence of demand flexibility. This player behaves as an energy aggregator, which manages energy and financial interactions between the market and DER organized in local energy systems (LES), which are in charge to satisfy the multienergy demand of a set of building clusters with flexible demand. A stochastic mixed-integer linear programming problem is formulated by considering uncertainties of intermittent DER facilities and day-ahead market price, to find the optimal bidding strategies while maximizing the expected aggregator's profit. Numerical results show that the method is efficient in finding the bidding curves in the day-ahead market through the optimal management of flexibility requests sent to clusters, as well as of DER in LES and interactions among LES. [ABSTRACT FROM AUTHOR]

Infrared scanning near-field optical microscopy (IR-SNOM) is an extremely powerful analytical instrument since it combines IR spectroscopy's high chemical specificity with SNOM's high spatial resolution. In order to do this in the infrared, specialty chalcogenide glass fibers were fabricated and their ends tapered to generate SNOM probes. The fiber tips were installed in a modified near-field microscope and both inorganic and biological samples illuminated with the tunable output from a free-electron laser located at Vanderbilt University. Both topographical and IR spectral images were simultaneously recorded with a resolution of ~ 50 and ~ 100 nm, respectively. Unique spectroscopic features were identified in all samples, with spectral images exhibiting resolutions of up to lambda/60, or at least 30 times better than the diffraction limited lens-based microscopes. We believe that IR-SNOM can provide a very powerful insight into some of the most important biomedical research topics. [ABSTRACT FROM PUBLISHER]

Bacteria evolved cell to cell communication processes to gain information about their environment and regulate gene expression. Quorum sensing is such a process in which signaling molecules, called autoinducers, are produced, secreted and detected. In several cases bacteria use more than one autoinducers and integrate the information conveyed by them. It has not yet been explained adequately why bacteria evolved such signal integration circuits and what can learn about their environments using more than one autoinducers since all signaling pathways merge in one. Here quantum information theory, which includes classical information theory as a special case, is used to construct a quantum gate circuit that reproduces recent experimental results. Although the conditions in which biosystems exist do not allow for the appearance of quantum mechanical phenomena, the powerful computation tools of quantum information processing can be carefully used to cope with signal and information processing by these complex systems. A simulation algorithm based on this model has been developed and numerical experiments that analyze the dynamical operation of the quorum sensing circuit were performed for various cases of autoinducer variations, which revealed that these variations contain significant information about the environment in which bacteria exist. [ABSTRACT FROM PUBLISHER]