The increasing penetration of renewable energy sources (RES) in power systems intensifies the need of enhancing the flexibility in grid operations in order to accommodate the uncertain power output ...of the leading RES such as wind and solar generation. Utilities have been recently showing increasing interest in developing Demand Response (DR) programs in order to match generation and demand in a more efficient way. Incentive- and price-based DR programs aim at enabling the demand side in order to achieve a range of operational and economic advantages, towards developing a more sustainable power system structure. The contribution of the presented study is twofold. First, a complete and up-to-date overview of DR enabling technologies, programs and consumer response types is presented. Furthermore, the benefits and the drivers that have motivated the adoption of DR programs, as well as the barriers that may hinder their further development, are thoroughly discussed. Second, the international DR status quo is identified by extensively reviewing existing programs in different regions.
In modern smart grids, the focus is increasingly shifted towards distributed energy resources and flexible electricity assets owned by prosumers. A system with high penetration of flexible prosumers, ...has a very large number of variables and constraints, while a lot of the information is local and non-observable. Decomposition methods and local problem solving is considered a promising approach for such settings, particularly when the implementation of a decomposition method features a market-based analogy, i.e. it can be implemented in a Transactive Energy fashion. In this paper we present an auction-theoretic scheme for a setting with non-convex prosumer models and resource constraints. The scheme is evaluated on a particular case study and its scalability and efficiency properties are tested and compared to an optimal benchmark solution. A game-theoretic analysis is made with respect to how an intelligent agent, that bids on behalf of a prosumer can try to strategize within the auction, in order to make itself better-off. Our simulations show that there is an alignment of incentives, i.e., when the prosumers try to strategize, they actually improve the auction's efficiency.
The proliferation of distributed energy assets necessitates the provision of flexibility to efficiently operate modern distribution systems. In this article, we propose a flexibility market through ...which the DSO may acquire flexibility services from asset aggregators in order to maintain network voltages and currents within safe limits. A max-min fair formulation is proposed for the allocation of flexibility. Since the DSO is not aware of each aggregator's local flexibility costs, we show that strategic misreporting can lead to severe loss of efficiency. Using mechanism design theory, we provide a mechanism that makes it a payoff-maximizing strategy for each aggregator to make truthful bids to the flexibility market. While typical truthful mechanisms only work when the objective is the maximization of Social Welfare, the proposed mechanism lets the DSO achieve incentive compatibility and optimality for the max-min fairness objective.
•Kernel density estimation is used to model the characteristics of the EV fleet.•A method to derive representative electricity price series is proposed.•Load duration curves can be closely ...approximated with few simulations.•EV parking lot load profile significantly varies with the employed strategy.
The electrification of the transportation sector through the introduction of electric vehicles (EV) has recently emerged as a remedy to environmental and economic concerns. For this reason, governments around the world have been offering subsidies and other benefits to drivers that replace their conventional vehicle with an EV in order to facilitate the commercialization of the latter. However, when compared to conventional vehicles, EVs present a key disadvantage that could hinder their widespread uptake: the time that is needed to charge an EV is in the range of hours. For this purpose, EV parking lots have been proposed in order to recharge vehicles at a higher rate. Recent studies indicate that vehicles remain parked for most of the day, implying that different operational strategies may be used in order to achieve operational or economic benefits from the perspective of the EV parking lot owner. The aim of this study is to derive representative load profiles of parking lots under different operational strategies. To perform so, the parameters of the EV fleet are modeled by estimating kernel distributions from available traffic data, while a time series transformation in combination with a clustering approach is used in order to obtain representative price patterns. The examined case studies demonstrate that by performing a reduced number of simulations regarding expected charging profiles of EV fleets, generalized results may be obtained using the proposed methodology.
This paper investigates the implementation of a community energy storage system (CESS) in a neighborhood consisting of households with flexible and inflexible loads, as well as photovoltaic power ...generation. The system incorporates overlay services (OS) such as fairness management, increasing the fairness level while maximizing CESS utilization, and reputation management, reducing the impact of households trying to take advantage of the system. Additionally, a credit scheme for households, referred to as the quota system, is introduced, allowing households to only import from the battery the amount of energy they have previously contributed. This quota system includes a way to trade credit with other households, as part of the OS. The case study shows that the introduction of the OS on top of a CESS of 50 kWh results in a drop in peak transformer load from 235.7% to 166.9%, with the peak load at 264.3% without a CESS present. Additionally, it is shown that introducing these OS in a CESS does not significantly impact the mean household cost.
Uncertainty characterization is an essential component of decision-making problems in electricity markets. In this work, a class-driven approach is proposed to describe stochasticity. The methodology ...consists of a three-step process that includes a class allocation component, a generative element based on a long short-term memory neural network and an automated reduction method with a variance-based continuation criterion. The system is employed and evaluated on Dutch imbalance market prices. Test results are presented, expressing the proficiency of the approach, both in generating realistic scenario sets that reflect the erratic dynamics in the data and adequately reducing generated sets without the need to explicitly and manually predetermine the cardinality of the reduced set.
In this paper, a detailed home energy management system structure is developed to determine the optimal day-ahead appliance scheduling of a smart household under hourly pricing and peak ...power-limiting (hard and soft power limitation)-based demand response strategies. All types of controllable assets have been explicitly modeled, including thermostatically controllable (air conditioners and water heaters) and nonthermostatically controllable (washing machines and dishwashers) appliances, together with electric vehicles (EVs). Furthermore, an energy storage system (ESS) and distributed generation at the end-user premises are taken into account. Bidirectional energy flow is also considered through advanced options for EV and ESS operation. Finally, a realistic test-case is presented with a sufficiently reduced time granularity being thoroughly discussed to investigate the effectiveness of the model. Stringent simulation results are provided using data gathered from real appliances and real measurements.
In this paper, the decentralized energy management of a community of prosumers is studied. The coordination of the prosumers participating in the community is cast as a mixed-integer linear program ...(MILP) with local and global coupling constraints. A dual decomposition method with a tightening of the coupling constraints is applied to solve the problem in a decentralized way. The resulting tightened problem provides feasibility guarantees for the obtained solutions. A receding horizon approach that exploits the structure of the problem is proposed to divide the problem in subproblems and solve them sequentially. The proposed approach ensures that the tightened problem is feasible under realistic conditions, whereas the existing methods may result in infeasible problems. Moreover, it may reduce the necessary constraint tightening compared to existing methods. Two setups are evaluated for the organizational structure of the community, one with a community coordinator and one without. Moreover, three different structures of the communication graph are analyzed for the setup without a coordinator. The simulation results demonstrate that the proposed method results in feasible problems that can be solved due to the reduced constraint tightening when other methods are not feasible. In the case of feasible problems, the optimality gap is comparable to that achieved with existing methods and even smaller for communities with a small number of participating prosumers.
Linear optimal power flow (OPF) formulations are powerful tools applied to a large number of problems in power systems, e.g., economic dispatch, expansion planning, state estimation, congestion ...management, electricity markets, among others. This article proposes a novel mixed-integer linear programming formulation for the ac-OPF of three-phase unbalanced distribution networks. The model aims to minimize the total energy production cost while guaranteeing the network's voltage and current magnitude operational limits. New approximations of the Euclidean norm, which is present in the calculation of nodal voltage and branch current magnitudes, are introduced by applying a linear transformation of weighted norms and a set of intersecting planes. The accuracy, optimality, feasibility, and scalability of the proposed linearizations are compared with common linear approximations in the literature using two unbalanced distribution test systems. The obtained results show that the proposed formulation is computationally more efficient (almost twice) while being as accurate and more conservative than the benchmarked approaches with maximum errors lower than 0.1%. Thus, its potential application in a variety of distribution systems operation and planning optimization problems is endorsed.
In modern power systems with high penetration of renewable energy sources, the flexibility provided by distributed energy resources is becoming invaluable. Demand aggregators offer balancing energy ...in the real-time balancing market on behalf of flexible resources. A challenging task is the design of the offering strategy of an aggregator. In particular, it is difficult to capture the flexibility cost of a portfolio of flexibility assets within a price-quantity offer, since the costs and constraints of flexibility resources exhibit inter-temporal dependencies. In this article, we propose a generic method for constructing aggregated balancing energy offers that best represent the portfolio's actual flexibility costs, while accounting for uncertainty in future timeslots. For the case study presented, we use offline simulations to train and compare different machine learning (ML) algorithms that receive the information about the state of the flexible resources and calculate the aggregator's offer. Once trained, the ML algorithms can make fast decisions about the portfolio's balancing energy offer in the real-time balancing market. Our simulations show that the proposed method performs reliably towards capturing the flexibility of the Aggregator's portfolio and minimizing the aggregator's imbalances.
PDF
