2,803 research outputs found
Techno-Economic Analysis and Optimal Control of Battery Storage for Frequency Control Services, Applied to the German Market
Optimal investment in battery energy storage systems, taking into account
degradation, sizing and control, is crucial for the deployment of battery
storage, of which providing frequency control is one of the major applications.
In this paper, we present a holistic, data-driven framework to determine the
optimal investment, size and controller of a battery storage system providing
frequency control. We optimised the controller towards minimum degradation and
electricity costs over its lifetime, while ensuring the delivery of frequency
control services compliant with regulatory requirements. We adopted a detailed
battery model, considering the dynamics and degradation when exposed to actual
frequency data. Further, we used a stochastic optimisation objective while
constraining the probability on unavailability to deliver the frequency control
service. Through a thorough analysis, we were able to decrease the amount of
data needed and thereby decrease the execution time while keeping the
approximation error within limits. Using the proposed framework, we performed a
techno-economic analysis of a battery providing 1 MW capacity in the German
primary frequency control market. Results showed that a battery rated at 1.6
MW, 1.6 MWh has the highest net present value, yet this configuration is only
profitable if costs are low enough or in case future frequency control prices
do not decline too much. It transpires that calendar ageing drives battery
degradation, whereas cycle ageing has less impact.Comment: Submitted to Applied Energ
A review on the virtual power plant: Components and operation systems
© 2016 IEEE. Due to the high penetration of Distributed Generations (DGs) in the network and the presently involving competition in all electrical energy markets, Virtual Power Plant (VPP) as a new concept has come into view, with the intention of dealing with the increasing number of DGs in the system and handling effectively the competition in the electricity markets. This paper reviews the VPP in terms of components and operation systems. VPP fundamentally is composed of a number of DGs including conventional dispatchable power plants and intermittent generating units along with possible flexible loads and storage units. In this paper, these components are described in an all-inclusive manner, and some of the most important ones are pointed out. In addition, the most important anticipated outcomes of the two types of VPP, Commercial VPP (CVPP) and Technical VPP (TVPP), are presented in detail. Furthermore, the important literature associated with Combined Heat and Power (CHP) based VPP, VPP components and modeling, VPP with Demand Response (DR), VPP bidding strategy, and participation of VPP in electricity markets are briefly classified and discussed in this paper
Participation of Electric Vehicle Aggregators in Wholesale Electricity Markets: Recent Works and Future Directions
Electric Vehicles are key to reducing carbon emissions while bringing a revolution to the transportation sector. With the massive increase of EVs in road networks and the growing demand for charging services, the electric power grid faces enormous system reliability and operation stability challenges. Demand and supply disparities create inconsistency in the smooth delivery of electrical power. As a potential solution, EVs and their charging infrastructure can be aggregated to prevent the unwanted effects on power systems and also facilitate ancillary services to the power grid. When not need for transportation purposes, EVs can leverage their batteries for power grid services by participating in the electricity market via mechanisms coordinated by system operators. Hence, the market participation of EV infrastructure can help alleviate the power grid stress during peak periods. However, further research is needed to demonstrate the multiple benefits to both EV owners and power grid operators. This paper briefly overviews the existing literature on market participation of EV aggregators, discuss associated challenges and needs, and propose research directions for future research
A two-stage stochastic bilevel programming approach for offering strategy of DER aggregators in local and wholesale electricity markets
A two-stage stochastic programming scheme is proposed in order to evaluate the offering strategy of a distributed energy resource aggregator in both wholesale and local electricity markets and appropriately cope with uncertainties associated with its decision-making problem. In this regard, the aggregator combines a broad range of virtual and real distributed energy resources to simultaneously participate in the local electricity market as a price-maker or strategic player and the wholesale electricity market as a price-taker or non-strategic player. To model the studied aggregator as a strategic entity in the local market, a bilevel programming approach is exploited in this work. Accordingly, at the upper level of the raised problem, the aggregator tends to promote its expected profit through taking part in the wholesale and local electricity markets, while at the lower level, the considered local market is cleared in a way to maximise the social welfare. In the end, the effectiveness of the proposed framework for the simultaneous participation of the distributed energy resource aggregator in these two markets has been explored utilising a case study.© 2022 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed
Virtual power plant models and electricity markets - A review
In recent years, the integration of distributed generation in power systems has been accompanied by new facility operations strategies. Thus, it has become increasingly important to enhance management capabilities regarding the aggregation of distributed electricity production and demand through different types of virtual power plants (VPPs). It is also important to exploit their ability to participate in electricity markets to maximize operating profits.
This review article focuses on the classification and in-depth analysis of recent studies that propose VPP models including interactions with different types of energy markets. This classification is formulated according to the most important aspects to be considered for these VPPs. These include the formulation of the model, techniques for solving mathematical problems, participation in different types of markets, and the applicability of the proposed models to real case studies. From the analysis of the studies, it is concluded that the most recent models tend to be more complete and realistic in addition to featuring greater diversity in the types of electricity markets in which VPPs participate. The aim of this review is to identify the most profitable VPP scheme to be applied in each regulatory environment. It also highlights the challenges remaining in this field of study
Systematic categorization of optimization strategies for virtual power plants
Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development
A Planner-Trader Decomposition for Multi-Market Hydro Scheduling
Peak/off-peak spreads on European electricity forward and spot markets are
eroding due to the ongoing nuclear phaseout and the steady growth in
photovoltaic capacity. The reduced profitability of peak/off-peak arbitrage
forces hydropower producers to recover part of their original profitability on
the reserve markets. We propose a bi-layer stochastic programming framework for
the optimal operation of a fleet of interconnected hydropower plants that sells
energy on both the spot and the reserve markets. The outer layer (the planner's
problem) optimizes end-of-day reservoir filling levels over one year, whereas
the inner layer (the trader's problem) selects optimal hourly market bids
within each day. Using an information restriction whereby the planner
prescribes the end-of-day reservoir targets one day in advance, we prove that
the trader's problem simplifies from an infinite-dimensional stochastic program
with 25 stages to a finite two-stage stochastic program with only two
scenarios. Substituting this reformulation back into the outer layer and
approximating the reservoir targets by affine decision rules allows us to
simplify the planner's problem from an infinite-dimensional stochastic program
with 365 stages to a two-stage stochastic program that can conveniently be
solved via the sample average approximation. Numerical experiments based on a
cascade in the Salzburg region of Austria demonstrate the effectiveness of the
suggested framework
Operational planning and bidding for district heating systems with uncertain renewable energy production
In countries with an extended use of district heating (DH), the integrated
operation of DH and power systems can increase the flexibility of the power
system achieving a higher integration of renewable energy sources (RES). DH
operators can not only provide flexibility to the power system by acting on the
electricity market, but also profit from the situation to lower the overall
system cost. However, the operational planning and bidding includes several
uncertain components at the time of planning: electricity prices as well as
heat and power production from RES. In this publication, we propose a planning
method that supports DH operators by scheduling the production and creating
bids for the day-ahead and balancing electricity markets. The method is based
on stochastic programming and extends bidding strategies for virtual power
plants to the DH application. The uncertain factors are considered explicitly
through scenario generation. We apply our solution approach to a real case
study in Denmark and perform an extensive analysis of the production and
trading behaviour of the DH system. The analysis provides insights on how DH
system can provide regulating power as well as the impact of uncertainties and
renewable sources on the planning. Furthermore, the case study shows the
benefit in terms of cost reductions from considering a portfolio of units and
both markets to adapt to RES production and market states
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