Scheduling Of Distributed Energy Resources Research Articles

Optimizing Distributed Energy Resources in Microgrid SCUC through Seq2Seq Scheduling Algorithms

In order to optimize Distributed Energy Resources (DERs) inside a microgrid's Security-Constrained Unit Commitment framework, this study investigates the use of Seq2Seq (Sequence-to-Sequence) scheduling methods (SCUC). There is a growing consensus that microgrids are an important part of the future of the electric grid because of the advantages they provide in terms of reliability, renewable energy integration, and overall efficiency. In the context of complicated SCUC issues, efficient scheduling and optimization of DERs are essential for realizing their full potential. Seq2Seq models, a kind of deep learning approach, have shown impressive performance in several applications requiring sequence prediction. Uncertainty in renewable energy production, energy demand forecasts, and security limitations are all addressed in this work as Seq2Seq algorithms are applied to microgrid SCUC. Significant gains in economic efficiency, ecological viability, and compliance with security constraints have been shown. This study lays the way for the development of more effective, sustainable, and resilient energy infrastructure by contributing to the advancement of the area of microgrid optimization.

Optimizing Distributed Energy Resources in Microgrid SCUC through Seq2Seq Scheduling Algorithms

In order to optimize Distributed Energy Resources (DERs) inside a microgrid's Security-Constrained Unit Commitment framework, this study investigates the use of Seq2Seq (Sequence-to-Sequence) scheduling methods (SCUC). There is a growing consensus that microgrids are an important part of the future of the electric grid because of the advantages they provide in terms of reliability, renewable energy integration, and overall efficiency. In the context of complicated SCUC issues, efficient scheduling and optimization of DERs are essential for realizing their full potential. Seq2Seq models, a kind of deep learning approach, have shown impressive performance in several applications requiring sequence prediction. Uncertainty in renewable energy production, energy demand forecasts, and security limitations are all addressed in this work as Seq2Seq algorithms are applied to microgrid SCUC. Significant gains in economic efficiency, ecological viability, and compliance with security constraints have been shown. This study lays the way for the development of more effective, sustainable, and resilient energy infrastructure by contributing to the advancement of the area of microgrid optimization.

Optimal Power Flow Management in Microgrids using Distributed Energy Resources

The effective and stable functioning of microgrids using distributed energy resources (DERs) is greatly dependent on ideal power stream management. To get the optimal power flow in microgrids, this study provides an inquiry into the use of cutting-edge optimization techniques. To fulfil load demand and consider DER requirements, system running costs must be kept to a minimum. The microgrid integrates a variety of distributed energy resources, including solar photovoltaic systems, wind turbines, and energy storage systems. The research examines several optimization strategies, such as mixed-integer programming, evolutionary algorithms, and linear programming, to decide on the best DER dispatch and scheduling. The findings give important information on how to efficiently use and manage distributed energy resources to raise the performance and resilience of microgrids.

A Multi-Agent Deep-Reinforcement-Learning-Based Strategy for Safe Distributed Energy Resource Scheduling in Energy Hubs

An energy hub (EH) provides an effective solution to the management of local integrated energy systems (IES), supporting the optimal dispatch and mutual conversion of distributed energy resources (DER) in multi-energy forms. However, the intrinsic stochasticity of renewable generation intensifies fluctuations in the system’s energy production when integrated into large-scale grids and increases peak-to-valley differences in large-scale grid integration, leading to a significant reduction in the stability of the power grid. A distributed privacy-preserving energy scheduling method based on multi-agent deep reinforcement learning is presented for the EH cluster with renewable energy generation. Firstly, each EH is treated as an agent, transforming the energy scheduling problem into a Markov decision process. Secondly, the objective function is defined as minimizing the total economic cost while considering carbon trading costs, guiding the agents to make low-carbon decisions. Lastly, differential privacy protection is applied to sensitive data within the EH, where noise is introduced using energy storage systems to maintain the same gas and electricity purchases while blurring the original data. The experimental simulation results demonstrate that the agents are able to train and learn from environmental information, generating real-time optimized strategies to effectively handle the uncertainty of renewable energy. Furthermore, after the noise injection, the validity of the original data is compromised while ensuring the protection of sensitive information.

Optimal Distributed Energy Resource Coordination: A Decomposition Method Based on Distribution Locational Marginal Costs

In this paper, we consider the day-ahead operational planning problem of a radial distribution network hosting Distributed Energy Resources (DERs) including rooftop solar and storage-like loads, such as electric vehicles. We present a novel decomposition method that is based on a centralized AC Optimal Power Flow (AC OPF) problem interacting iteratively with self-dispatching DER problems adapting to real and reactive power Distribution Locational Marginal Costs. We illustrate the applicability and tractability of the proposed method on an actual distribution feeder, while modeling the full complexity of spatiotemporal DER capabilities and preferences, and accounting for instances of non-exact AC OPF convex relaxations. We show that the proposed method achieves optimal Grid-DER coordination, by successively improving feasible AC OPF solutions, and discovers spatiotemporally varying marginal costs in distribution networks that are key to optimal DER scheduling by modeling losses, ampacity and voltage congestion, and, most importantly, dynamic asset degradation.

Impact of distribution tariffs on prosumer demand response

Distributed energy resources (DERs) may enable prosumers to deliver demand response under dynamic energy pricing schemes. Facing wholesale market prices, the DER scheduling of a profit-maximising prosumer minimises the total system energy cost as well. However, regulated electricity bill components, i.a., distribution tariffs for recovering grid costs, may distort these price signals. To study the impact of distribution tariff structures on DER scheduling, we develop a short-term linear optimisation model that determines the cost-optimal DER schedule based on wholesale electricity prices and a distribution tariff. We compare five distribution tariff structures for four combinations of PV, batteries, and heat pumps, and define two metrics to characterise the results: (i) the novel ‘relative flexibility value’, which quantifies energy cost inefficiencies, and (ii) ‘rate of self-consumption’. In a case study, we show how prosumers make trade-offs between energy and distribution costs, depending on the distribution tariff structure. We describe the mechanisms through which different distribution tariff structures alter DER operations, and quantify the tariffs' impact on the system energy cost. Generally, we find that tariff types which stimulate self-consumption increase the system energy cost and vice versa. • Distribution tariffs distort the scheduling of distributed energy resources. • Prosumers trade off energy cost increases with distribution cost decreases. • Self-consumption can reduce grid interaction and related private costs. • Tariffs with high self-consumption perform low on cost-efficiency and vice versa.

Distributed Energy Resource Scheduling with Focus on Demand Response Complex Contracts

Demand response (DR) is a flexible way to improve distributed energy resource scheduling. The innovative contribution of this paper is to include complex contracts in the model, which can accommodate the constraints according to the special expectations of each player. Such contracts are included in the optimization of distributed energy resource scheduling to dispatch DR according to the expectations of consumers. Multi-period DR events are considered. In this way, consumers can specify the limits on the time, power, and remuneration regarding participation in DR events, which has not been considered in the literature. The state of the art treats these aspects separately or uses a statistical approach, without providing consumers with options to combine their preferences regarding different aspects of their flexibility deployment. The model has been validated for 218 consumers using several scenarios and different types of distributed generation, showing that it is possible to increase DR with respect to the preferences of consumers.

Toward a Retail Market for Distribution Grids

Modern active distribution grids are characterized by the increasing penetration of distributed energy resources (DERs). Proper coordination and scheduling of these DERs requires a local retail market which can operate at the distribution grid level. In this paper, we propose a retail market for optimally managing and scheduling DERs, and coordinating ancillary services in a distribution grid. Our proposed retail market leverages a recently proposed distributed proximal atomic coordination (PAC) algorithm which has several advantages over other distributed algorithms, with reduced local computational effort and enhanced privacy. We describe how the market can be implemented using a Distribution System Operator (DSO), whose representatives are located at the primary feeder level and workers are located at the substation level, and how the DSO will interact with the Wholesale Electricity Market. Finally, we extensively validate the performance of the proposed retail market via simulations of three networks: a real distribution grid in Tokyo, a balanced IEEE 123-bus distribution grid, and a modified IEEE 13-bus network. Our results show that the proposed market is practical and can be easily implemented in distribution grids, resulting in optimal real-time scheduling of DERs and compensation in the form of distributed locational marginal prices.

Integrated Electricity– Heat–Gas Systems: Techno–Economic Modeling, Optimization, and Application to Multienergy Districts

Multienergy systems (MES) can optimally deploy their internal operational flexibility to use combinations of different energy vectors to meet the needs of end-users and potentially support the wider system. Key relevant applications of MES are multienergy districts (MEDs) with, for example, integrated electricity and gas distribution and district heating networks. Simulation and optimization of MEDs is a grand challenge requiring sophisticated techno–economic tools that are capable of modeling buildings and distributed energy resources (DERs) across multienergy networks. This article provides a tutorial-like overview of the state-of-the-art concepts for techno–economic modeling and optimization of integrated electricity–heat–gas systems in flexible MEDs, also considering operational uncertainty and multiple grid support services. Relevant mixed integer linear programming (MILP) formulations for two-stage stochastic scheduling of buildings and DER, iteratively soft-coupled to nonlinear network models, are then presented as the basis of a practical network-constrained MED energy management tool developed in several projects. The concepts presented are demonstrated through real-world applications based on The University of Manchester MED case study, the details of which are also provided as a testbed for future research.

A bi-level model for optimal bidding of a multi-carrier technical virtual power plant in energy markets

Distributed energy resources (DERs) play an important role in the future vision of distribution energy networks. This paper with the aim of promoting DERs role in energy markets proposes a new framework for the optimal bidding strategy of a Technical Virtual Power Plant (TVPP) in different markets. In the proposed framework, it is assumed that the TVPP energy power is distributed throughout a multi-carrier energy network (MCEN) which consists of district heat networks (DHNs) and a distributed electricity network (DEN). In the MCEN, CHP units are the linkage between DHNs and DEN. With the goal of maximizing the profit of TVPP in different local and wholesale energy markets, a bi-level optimization model is formularized to set scheduling of DERs. As the upper level, the TVPP sets the schedule of its DERs considering technical constraints of DHNs and DENs. In the lower level, however, the TVPP runs an optimization problem to estimate the energy price in wholesale energy markets considering the forecasted bids of other rivals. The proposed model is implemented on a test system and the results confirms that the local market creates an opportunity for CHP owner, TVPP and the heat consumers to make more profit as well as achieving better technical performance for distribution energy systems.

Network reconfiguration and distributed energy resource scheduling for improved distribution system resilience

Electric utility companies work to restore as much load as possible after power outages caused by extreme weather events. In this paper, an outage management strategy is proposed to enhance distribution system resilience through network reconfiguration and distributed energy resources (DERs) scheduling. After a line fault, the proposed algorithm can identify radial network topology based on the rank of the incidence matrix. The reconfiguration is implemented by switching tie lines and sectionalizing lines. With the new network topology, an optimal DER scheduling problem is solved to minimize the accumulative cost for dispatchable DER operation and load reduction. Finally, the optimal topology that minimizes the accumulative cost is selected from all radial topologies. The computational workload is relatively low because only linear programming needs to be solved. Using the case studies of the IEEE 69-bus and IEEE 123-bus systems, we consider the worst-case scenarios in which faults occur in the upstream feeder. The simulation results demonstrate that the proposed strategy allows for a relatively high percentage of the load to remain in service after line faults. Furthermore, compared with microgrid-formation approaches, the proposed strategy has advantages when applied to the distribution systems with several normally-open tie lines and low DER penetration.

Distributionally Robust Chance-Constrained Transactive Energy Framework for Coupled Electrical and Gas Microgrids

In this article, the transactive energy as a sustainable technology is introduced for the integration of numerous distributed energy resources (DERs) in a reliable manner. DERs can actively participate in the day-ahead (DA), real-time balancing (RTB), and wholesale gas markets for achieving the various goals. This article proposes a distributionally robust chance-constrained (DRCC) model based on the transactive energy approach for the optimal scheduling of microgrids in coupled electrical and gas networks. Optimal scheduling of DERs has been carried out for maximizing the microgrids' profits in the DA electricity and gas markets while minimizing the imbalance costs is an objective for microgrids in the RTB market. Transactive energy technology is used for managing energy exchange between microgrids and the power grid. Moreover, the linearization techniques are employed for avoiding nonlinear equations to obtain reliable results in a short time. Due to the effects of the electrical and gas networks on each other, the interactions between them are considered by investigating both the electrical and gas energy conversions in the real system. Simulation results are extracted considering two cases: Case I without DRCC and Case II with DRCC. Given the results, the cost of interrupted load, DERs, and gas sector in Case II are, respectively, reduced to 35.4%, 25.02%, and 17.1% in comparison with Case I. Moreover, the DRCC method guarantees the achievement of $169027.759 profit for microgrids, which is reduced to 47.85% in comparison with the base case.

Transmission Network Investment With Distributed Energy Resources and Distributionally Robust Security

Distributed energy resources (DER) have the potential to significantly contribute to network security and hence release latent capacity of existing transmission assets. In this context, we propose a distributionally robust approach to network security in order to recognize the limited data and knowledge associated with the underlying process behind the realization of system contingencies within the transmission expansion planning (TEP) problem, and thus determine the optimal portfolio of DER services necessary to displace, in a secure fashion, inefficient network investments. To do so, we propose a two-stage optimization model where the first stage determines the transmission expansion plan and the scheduling of DER post-contingency services in coordination with further corrective control measures such as generation reserves. The second stage minimizes the expected cost of corrective actions under various contingencies. Through various case studies, we demonstrate the benefits of security services provided by DER and the advantages of our proposed distributionally robust approach (where outage rates are assumed ambiguous) against alternative $n-K$ security and stochastic approaches, where outage rates are either ignored or assumed fully known, respectively.

Energy Crowdsourcing and Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids

The power grid is rapidly transforming, and while recent grid innovations increased the utilization of advanced control methods, the next-generation grid demands technologies that enable the integration of distributed energy resources (DERs)---and consumers that both seamlessly buy and sell electricity. This paper develops an optimization model and blockchain-based architecture to manage the operation of crowdsourced energy systems (CES), with peer-to-peer (P2P) energy trading transactions. An operational model of CESs in distribution networks is presented considering various types of energy trading transactions and crowdsourcees. Then, a two-phase operation algorithm is presented: Phase I focuses on the day-ahead scheduling of generation and controllable DERs, whereas Phase II is developed for hour-ahead or real-time operation of distribution networks. The developed approach supports seamless P2P energy trading between individual prosumers and/or the utility. The presented operational model can also be used to operate islanded microgrids. The CES framework and the operation algorithm are then prototyped through an efficient blockchain implementation, namely the IBM Hyperledger Fabric. This implementation allows the system operator to manage the network users to seamlessly trade energy. Case studies and prototype illustration are provided.

Day-ahead stochastic multi-objective economic/emission operational scheduling of a large scale virtual power plant

The reduction of global greenhouse gas emissions is one of the key steps towards sustainable development. The integration of Distributed Energy Resources (DERs) in power systems will help with emissions reduction. Virtual Power Plants (VPPs) can overcome barriers to participation of DERs in system operation. In this paper, a model is proposed for the energy management of a VPP including PhotoVoltaic (PV) modules, wind turbines, Electrical Energy Storage (EES) systems, Combined Heat and Power (CHP) units, and heat-only units. The multi-objective operational scheduling of DERs in the VPP focuses on maximizing the expected day-ahead profit of the VPP and minimizing the expected day-ahead emissions. The uncertainty of wind speed, solar radiation, market price, and electrical load is modeled using scenario based approach. Also, two-stage stochastic programming is implemented for modeling the VPP energy management. Three cases have been investigated for evaluating the proposed method: single-objective scheduling of VPP to maximize profit, single-objective scheduling of VPP to minimize emission and multi-objective economic/emission scheduling of VPP. The results indicate the appropriate economic and environmental performance of the proposed method, which provides the possibility of selecting a compromise solution for the VPP operator in accordance with environmental restrictions and economic constraints.

Control of Networked Distributed Energy Resources in Grid-Connected AC Microgrids

This paper focuses on the optimal utilization of grid-connected microgrids for providing frequency regulation services to the bulk systems they are connected to. We consider microgrids comprised of several distributed energy resources (DER) interconnected via an electrical network. We then formulate a joint DER scheduling and power-flow optimization problem in order to determine the DER setpoints so that the cost of failing to follow some regulation signal is minimized in each regulation contract; this problem is nonconvex. We propose a convex relaxation for this problem, and propose a family of continuous-time control laws, the trajectories of which can be computed in a distributed way. We prove that these converge to an optimal solution of the relaxed problem, and that they result in feasible power flows for the system at each time instant. The proposed continuous-time control laws have to be implemented in a discrete-time fashion over some communication network. We propose a discrete-time approximation of our control laws, and show that the resulting discrete-time control laws converge to an optimal solution of the relaxed problem.

Economic-Environmental Optimal Management of Smart Residential Micro-Grid Considering CCHP System

Due to issues such as air pollution and global warming, Distributed Energy Resources (DERs) including Distributed Generations (DGs), Energy Storage Systems (ESSs) and Demand Response Programs (DRPs) are gaining increasing attention to reduce dependency on fossil fuels as well as environmental pollution. In this article, a multi-objective optimization of economic environmental scheduling of DERs in a smart residential Micro-Grid (MG) considering numerous constraints and Demand Response Programs (DRPs) has been presented. To optimize the aforementioned objective functions, a new algorithm called Adaptive Fuzzy Multi-Objective Particle Swarm Optimization (AFMOPSO) has been proposed. This algorithm has a great capability to avoid local optimal solutions by finding a compromise between the general and local search within the decision making area. In addition, a comprehensive Combined Cooling Heating and Power (CCHP) system based on various thermodynamic equations has been proposed. The results demonstrate the efficient performance of the proposed algorithm compared to the other traditional optimization methods via some diversity and spacing criteria.

Optimal Scheduling for Prosumers in Coupled Transactive Power and Gas Systems

To help the integration of various distributed energy resources (DERs), this paper proposes a transactive approach to the optimal scheduling for prosumers in coupled energy systems. DERs are coordinately operated in the form of a virtual power plant (VPP), which actively participates in the day-ahead and real-time electricity market, as well as the wholesale gas market. In the day-ahead electricity and wholesale gas markets, a VPP aims to maximize expected profits by determining the unit commitments and hourly scheduling of DERs. In the real-time balancing market, a VPP adjusts DER schedules to minimize imbalance costs. In this paper, we address the energy conversions between electric power and gas loads and investigate the interacting operations of gas and power systems. Our simulation results show that hierarchical, coordinated power and gas scheduling can identify more accurate operation plans for coupled transactive energy systems.

Two-Stage Coordinated Operational Strategy for Distributed Energy Resources Considering Wind Power Curtailment Penalty Cost

The concept of virtual power plant (VPP) has been proposed to facilitate the integration of distributed renewable energy. VPP behaves similar to a single entity that aggregates a collection of distributed energy resources (DERs) such as distributed generators, storage devices, flexible loads, etc., so that the aggregated power outputs can be flexibly dispatched and traded in electricity markets. This paper presents an optimal scheduling model for VPP participating in day-ahead (DA) and real-time (RT) markets. In the DA market, VPP aims to maximize the expected profit and reduce the risk in relation to uncertainties. The risk is measured by a risk factor based on the mean-variance Markowitz theory. In the RT market, VPP aims to minimize the imbalance cost and wind power curtailment by adjusting the scheduling of DERs in its portfolio. In case studies, the benefits (e.g., surplus profit and reduced wind power curtailment) of aggregated VPP operation are assessed. Moreover, we have investigated how these benefits are affected by different risk-aversion levels and uncertainty levels. According to the simulation results, the aggregated VPP scheduling approach can effectively help the integration of wind power, mitigate the impact of uncertainties, and reduce the cost of risk-aversion.

Agent-Based Microgrid Scheduling: An ICT Perspective

New Information and Communications Technologies (ICT), such as the Internet of Things (IoT), are enabling the evolution of energy grids towards a sophisticated power network called Smart Grid (SG). In the context of SGs, a microgrid is a self-sustained network that can operate in both grid-connected or stand-along modes. The long-term scheduling of the operation of distributed generators (DG) and renewable energy resources (RES) in microgrids is a problem that requires tough planning and the use of advanced tools to be efficiently addressed. This paper discusses different ICT technologies that can enable microgrid communication for control and management of distributed energy resources (DER). Based on such ICT, we propose a novel agent-based model to address the long-term scheduling of DER in microgrids as a distributed constraint optimization problem (DCOP). However, finding the optimal solution for a DCOP is known to be an NP-Hard problem, making difficult to guarantee optimal solutions even for short optimization periods. Hence, for the long-term scheduling of DER, we propose to split the problem into small time windows that can be effectively solved sequentially by off-the-shelf DCOP algorithms. A particular, but general enough case study is used to compare different DCOP algorithms under the proposed model. Results show that DCOP algorithms can find optimal and near-optimal solutions depending on the window size and the scenario considered.