Day-ahead Energy Market Research Articles

Optimal self-scheduling for a multi-energy virtual power plant providing energy and reserve services under a holistic market framework

This paper addresses a self-scheduling model for a multi-energy virtual power plant (MEVPP) to optimize its day-ahead energy and reserve schedules considering the participation in joint markets. The coordination of energy and reserve services is realized by developing a holistic market framework. MEVPP trades electric energy in day-ahead market and natural gas in natural gas market under the uniform price scheme. MEVPP provides reserve in ancillary service market under the pay-as-bid scheme considering uncertain market clearing prices. Reserve regulations are modeled for the reserve quality provided by MEVPP. MEVPP can sign contracts in capacity market for capacity adequacy. The electricity and natural gas imbalance payments and unsupplied reserve penalty resulting from uncertain PV generation are calculated in real-time. The case studies, based on the practical data from Italian power exchange and transmission system operator, show the economic achievements of MEVPP with multiple markets participation. The advantages of multi-energy coupling in improving flexibility and economic profit are numerically analyzed. MEVPP is proven to be a promising reserve service supplier for TSO. Because through reasonable regulations, the reserve quality of MEVPP can be improved with little impact on its total cost.

Ameliorated class topper optimizer for cost optimization using demand side management program in a day-ahead energy market

Ameliorated class topper optimizer for cost optimization using demand side management program in a day-ahead energy market

Bidding strategy of the virtual power plant considering green certificates and carbon trading

This paper examines the impact of green certificates and carbon trading on virtual power plants (VPPs) in the day-ahead energy and frequency modulation assistance service combined market. VPPs, which integrate numerous distributed energy systems and adjustable loads, provide a new idea for coordination management among multiple subjects in market competition. The study proposes a bidding strategy model to maximize VPP economic benefits, considering the interaction mechanism of green certificate-carbon trading and market environment constraints. The proposed model is verified through simulation cases, in which different scenarios of the existence of green certificates and carbon trading mechanism are considered. The impact of green certificates and carbon trading on VPP bidding benefits is explored, and the results show that VPPs have superiority compared to the traditional bidding output of a VPP. The availability of green certificates and carbon trading enables the economic income of VPPs to be more diversified and reflects the low-carbon attribute of the renewable energy system, thereby promoting the low-carbon transformation process of the VPP output structure.

Bidding strategy for wind power and Large-scale electric vehicles participating in Day-ahead energy and frequency regulation market

Aiming at the problem of insufficient research on the interactions of various participants in energy and frequency regulation (FR) market that takes into account the participation of wind power (WP) and large-scale electric vehicles (EV), a bidding strategy for WP and large-scale EVs in day-ahead energy-FR market is proposed in this paper. Firstly, based on the analysis of the influence factors of the whole process EV behavior boundaries, a classification and aggregation method of EV cluster is proposed. Then, considering EV battery loss and wind power deviation penalty, a two-layer model is established. The upper layer is the bidding model of maximum revenue of wind power producer (WPP) and electric vehicle aggregator (EVA), and the lower layer is the clearing model with the lowest system operation cost for the power trading center. The competitive relationship between EVA and WPP is described based on Nash game. Finally, the simulated results show that the suggested classification and aggregation method can achieve fast and accurate solution of large-scale electric vehicles optimization model, and the independent bidding mode is more consistent with the optimal market operation compared with the price taker bidding mode and the collaborative bidding mode, which can protect the benefit for each participant.

Optimal sizing of hybrid wind-photovoltaic plants: A factorial analysis

This research attempts to determine the optimal size (in terms of profitability) of a photovoltaic (PV) plant that is going to be added to an existing wind installation. The analysis carried out is based on a real sample of 62 wind facilities located in 25 Spanish provinces in 2021. Given the hourly energy generated throughout the year by the wind facility and its grid capacity, the optimal power of the PV plant in the hybrid facility will be the one that allows maximising the net present value of the investment, i.e., the one that allows to better adjust (in economic terms) the PV production to the characteristics of the wind farm. For our empirical analysis we need data on the day-ahead energy market price, on the grid capacity and hourly production (in the day-ahead energy market) of the 62 wind farms analysed, and on the hourly PV production in the Spanish provinces where those wind farms are located. In average terms, to maximise the Net Present Value (NPV) per € invested, the optimal PV power to be added to an existing wind farm should be 8% of the wind power already installed. The averages of the financial indicators for optimal PV sizing are promising (discount rate of 7%): NPV per € invested is 1.89, NPV is M€ 23.5, discounted payback is 4.85 years, and the internal rate of return index is 25.6%. To conclude our empirical analysis, we estimate a multilevel regression model for the hourly wind production. The regression model shows, among other results, that one more MW of wind power translates into an increase in wind generation of 0.31 MWh. Our findings will help the design of hybrid plants without neglecting the economic aspect.

Two-Stage Participation of Grid-Connected Energy Hubs in Day-Ahead and Real-Time Energy Markets Based on Robust Modeling of Uncertainties

Optimal management of sources, storage, and responsive loads in the energy network leads to improvement of economic and technical situation of the network. However, note that single-layer energy management requires to provide the network operator with more amounts of information. This results in the increase in the processing speed for the network operator. Also, coordination of sources, storage, and responsive loads in the form of an aggregator such as an energy hub can play an important role for them in promoting the economic situation and energy efficiency. So, this paper presents the participation of grid-connected energy hubs (EHs) in the day-ahead (DA) and real-time (RT) energy markets. Scheme has a two-stage optimization framework. In the first stage, participation of EHs in the hourly DA energy market is executed based on a two-layer energy management system that is modeled as bilevel problem. EH model in DA market is formulated in upper-level problem aimed at maximizing their profit subject to meeting the linearized constraints of the EH’s operation. Lower-level problem deals with the operation of energy networks to minimize cost of energy purchase from the upstream network constrained to linearized optimal power flow equations. The second stage of participation of EHs in a 5 min RT market has an objective function to minimize the unbalance between the EHs’ profits in the mentioned two markets. This stage is subject to the upper-level constraints and the lower-level formulation in the first stage. Robust optimization models the uncertainties of load, renewable power, energy price, and energy demand of mobile storage to achieve an optimal robust solution. Benders decomposition algorithm solves bilevel formulation in two-stage scheme. Numerical results confirm capabilities of the scheme in enhancing operation status of the networks and economic situation of EHs. Hence, the Benders decomposition algorithm is able to extract the optimal solution in shorter computation time. Two-layer energy management needs shorter computation time than the single-layer management. Simultaneous modeling of two real-time and day-ahead energy markets reduces the unbalance cost between these markets. The proposed scheme can enhance the operating and economic indicators of the network by up to around 18% and 26% compared to power flow studies of energy networks.

A proposed hedge-based energy market model to manage renewable intermittency

Renewable energy power producers are exposed to significant volatility in revenue due to intermittency associated with wind and solar energy. Moreover, higher penetration of variable renewable energy in the electric grids is significantly increasing the cost associated with procuring reserves, resulting in higher electricity costs to consumers. Hence, a market that can appropriately distribute the cost of procuring reserves whilst ensuring revenue stability for renewable energy producers is crucial. In this work, the authors propose a novel hedge-based energy market model that allows renewable generators to secure hedge contracts from flexible generating technologies as insurance against weather-driven energy deficits. The proposed model supplements a representative day-ahead market model and maximizes the revenue of market participants whilst diminishing the costs of procuring reserves and generating investment signals for green projects. A mathematical model is formulated to determine market equilibrium based on the Karush Kuhn Tucker (KKT) optimality conditions. Simulation studies are carried out to demonstrate the efficacy of the proposed model on a test network using MATLAB. The theoretical results are verified by simulation results and provide a feasible region in which mutually acceptable hedge contracts result in higher overall revenues. The results show that a hedge-based energy market model can be deployed to manage renewable intermittency in a day-ahead energy market model to address the risk management needs of renewable power producers.

Combined Provision of Economic Dispatch and Frequency Regulation by Aggregated EVs Considering Electricity Market Interaction

A combined delivery of economic dispatch and frequency regulation (FR) services from electric vehicles (EVs) is proposed to exploit the dynamic flexibility of EV charging load and its revenue opportunities in the electricity markets. The economic feasibility is assessed with the combined operation model of EVs for optimal bidding in the day-ahead energy market and FR market. The proposed optimization adopts the hierarchical control framework with EV aggregator (EVA) as the intermediate control unit to interact with the power system operator. As EVs are paid for FR based on their actual performance, a dynamic control strategy for charging EVs in response to regulation signals is developed to validate the technical feasibility of combined operation. The autonomous control of EVA modulates the charging of each EV to ensure the optimal charging schedules and real-time frequency control. A case study reveals that the profitability of EV charging control can be significantly increased by energy cost reduction and additional regulation revenue. The proposed dynamic control with a novel power-sharing strategy of EVA provides effective frequency support based on the optimal charging schedule and capacity bids without violating charging demand and availability constraints of each individual EV.

A Multi-Agent based simulator for strategic bidding in day-ahead energy market

A Multi-Agent based simulator for strategic bidding in day-ahead energy market

A Novel Unidirectional Smart Charging Management Algorithm for Electric Buses

The difficulty of controlling the charging of electric buses (EBs) and their effects on network demand are discussed in this study. The solutions suggest a call for worldwide, complex infrastructures that manage EVs and EBs equally. Additionally, the Distribution Network (DN) must be prepared for an increased prevalence of reverse power flow caused by widespread distributed renewable generation. This paper focuses exclusively on EBs since they have higher capacity and predictable charging patterns, which makes them more significant for the DN in the context of a transition to complete vehicle electrification and technologies that are mature enough to be hosted. The proposed algorithm employs the Day-Ahead Energy Market (DAEM) in the Smart Charging (SC) to forecast the network operating circumstances. Additionally, the technique makes it possible to facilitate distributed photovoltaic (PV) generation, allowing network demand to be referenced depending on net demand. It also identifies an appropriate individual charger current per vehicle and per-time-step with load-levelling or peak-shaving as its primary goal. The final real demand demonstrates that a coarse correction of the demand is possible. According to the analysis of the DN voltage profile and associated line losses, the ideal node position location of the CS is dependent on PV penetration.

Optimal active load scheduling in a day-ahead energy market with uncertainty in demand

The existing power loads are continuously increasing and leading to various challenges related to economics and systems constraints. Any uncontrolled fluctuations of the demand over consecutive hours would dramatically complicate the correct management of the power generation. Therefore, this paper provides an effective solution for managing the uncertainty in loads and providing optimal scheduling of the power generation based on active load optimization in the day-ahead energy market. The proposed optimization model relies on operating active loads to encounter any unexpected change in the load pattern with taken into consideration the characteristics of these loads. The objective of the optimization model is to procure the lowest energy bill by reducing operational costs by taking into account the compensation cost in case of operating the active loads. The optimized problem is solved using mixed-integer linear programming through two technical stages. The first stage handles the normal operation of generation and passive demand, while the second stage treats all the uncertainty in stochastic scenarios. The active loads are operated under specific constraints such as the instantaneous min/max amount and the min/max duration over 24-h period of time. Case studies are used to demonstrate the effectiveness of implementing active loads.

A bi-level approach for participation of hybrid transmission operating companies in the day-ahead market, considering energy storage systems

This paper presents a new approach for participation strategy of Hybrid Transmission Operating Companies (HTOCs) in the day-ahead energy market. In this paper HTOC refers to private companies that have won physical transmission rights (PTR) and also own energy storage facilities. In the introduced hybrid structure, the HTOC can make profit in two different ways; first, through the flow of transmission lines (the MW-Mile transmission pricing method is used here); and second, through an optimum scheduling of its energy storage system. The optimum charge/discharge pattern of energy storage systems, evaluated in this paper, would also increase the MW-Mile profit of the company. The proposed method is a bi-level optimization model. In the upper level the profit of the HTOC is maximized and in the lower level the market clearing process of the independent system operator (ISO) has been modeled. The uncertain behavior of rival units is modeled using robust optimization (RO) method. The Karush-Kuhn-Tucker (KKT) optimality conditions are implemented to convert the proposed bi-level problem into its equivalent single-level problem. The Big-M method and the strong duality theorem (SDT) are used to linearize the proposed method. To demonstrate the performance of the introduced structure for HTOC, the proposed strategy has been implemented on a standard IEEE 9-bus and 57-bus test systems. The results show that HTOC can achieve a maximum guaranteed profit using storage resource (SR). It is also concluded that the presence of SR has not only increased HTOC's profit but also helped to make the structure more flexible against the uncertain parameters. In addition, the RO as a calculation risk method has been perfectly matched with the implementation of SR as a source of hedging uncertainties in managing the risk.

Optimal management and data-based predictive control of district heating systems: The Novate Milanese experimental case-study

This paper addresses the modelling and optimal control of district heating systems connected to the electrical grid, with the goal of maximizing their operational efficiency and enabling the participation to electricity markets. Being these systems governed by nonlinear large-scale dynamical models, a novel procedure is proposed, which enables to obtain suitable models for optimization, and consisting in a combination of physical and identified piece-wise linear models. A two-phase optimization and control scheme is then designed, including an offline scheduling problem for participating to the day-ahead energy market, and an online Model Predictive Control system, minimizing the energy consumption of thermal generators while properly satisfying the users thermal demand. The proposed methodology is developed considering a real district heating plant, owned by the energy company A2A S.p.A. and supplying the city of Novate Milanese (Italy), and different experiments on the plant have been carried out. The experimental results and achieved performances are promising, showing a significant reduction of the operational costs and overall gas consumption.

Optimal offering strategy for wind-storage systems under correlated wind production

This paper formulates the offering problem for a cluster of wind-storage systems in the day-ahead energy market using a risk-constrained stochastic programming approach that anticipates different operating conditions in the real-time energy market. Wind-storage systems can be jointly operated as a cluster so as to achieve higher profitability. However, a meaningful positive correlation among the production provided by wind farms located in the cluster results in a higher level of uncertainty that imposes additional risk. A key issue is how this correlation influences the operation of the cluster in the energy markets. In order to study this subject, this paper presents the uncertainties involved by means of a number of correlated scenarios including: (i) the correlated prices in the day-ahead and the real-time markets, and (ii) the correlated wind power production of multiple wind farms jointly generated using an innovative scenario generation methodology. The comparative statistical analysis validates the good accuracy of the method proposed in order to capture the spatio-temporal correlation among the wind farms. The results of a realistic case study are, moreover, compared with those obtained by considering that the scenarios are generated individually for each wind farm. Upon considering the latter, the variability of wind power production is underestimated, which has a negligible impact on the expected profit; however, the profit risk modeled using the conditional value-at-risk is significantly overestimated. The overestimation error particularly concerns a less risk-averse operator of the cluster in the case of low wind power production.

A market decision-making model for load aggregators with flexible load

The fast development of renewable energy has resulted in great challenges to the power system, which urgently needs more flexible resources to maintain a system supply/demand balance. This paper established a multi-stage electricity market framework in the presence of a load aggregator (LA) including a day-ahead energy/reserve market and a real-time balanced market. To actively participate in the day-ahead energy market and reserve market, a load profile perception model for LA is proposed to evaluate in detail the response performance of consumers. Meanwhile, a market-bidding model of LA and a market-clearing model of the system operator for the day-ahead market are also established. To actively join the real-time balance market, a market-bidding model of LA for the real-time balance market based on surplus flexible resources is established. The system operator further clears the real-time balance market and dispatches the collected flexible resources according to the system supply-demand state. A modified IEEE 30 bus system is tested and shows that the proposed market framework can effectively promote consumers to respond to system regulation requirements and lowers the system supply-demand imbalance risk.

IGDT-Based Robust Operation of Thermal and Electricity Energy-Based Microgrid with Distributed Sources, Storages, and Responsive Loads

In this paper, the optimal operation of microgrids (MGs) with thermal blocks, distributed generations (DGs), storage systems, and responsive loads is presented to achieve optimal scheduling of active, reactive, and thermal power of the mentioned elements in the day-ahead (DA) reactive power and energy market environment. The thermal block has a combined heat and power (CHP) system, a boiler, and thermally responsive loads. This scheme minimizes the difference between the total operating costs of the MG and power sources and the total revenue gained from the sale of energy and reactive power of the mentioned elements in the markets located in the MG. It is constrained by the AC power flow equations, network operation constraints, and the operating model of these elements. Furthermore, this scheme is subject to the uncertainties of energy price, load, and renewable power. In this paper, to access the optimal resistant solution against the maximum prediction error associated with the mentioned uncertainties, a robust model based on information gap decision theory (IGDT) is used. Finally, by implementing the proposed scheme on a 119-bus radial MG, the obtained numerical results confirm the ability of the scheme to simultaneously improve the economic and operational situation of the MG. The proposed scheme succeeded in improving energy cost, energy loss, voltage drop, and power factor of the distribution substation by roughly 101%, 44%, 41%, and 16% compared to power flow studies, even in the worst-case scenario caused by uncertainties.

Transmission Congestion Management in Deregulated Power System Using Adaptive Restarting Genetic Algorithm

Power systems in a deregulated environment have more intense and recurrent transmission line congestion than conventionally regulated power systems. With the help of generation rescheduling, this article shows how to effectively manage congestion in the day-ahead energy market by taking corrective measures to reduce congestion. The research employs an adaptive restarting genetic algorithm (ARGA) to provide an effective congestion management strategy in a deregulated power market (DPM). The study makes two significant contributions. First, the generator sensitivity factors (GSF) are calculated to choose re-dispatched generators. Second, the least congestion cost is calculated using the adaptive restarting genetic algorithm. Several different line outage contingency cases on IEEE 30 bus systems are used to examine the suggested algorithm’s implementation efficacy. The simulation results demonstrate a significant reduction in net congestion costs, resulting in a more reliable and secure power system operation. The proposed algorithm was tested in a python environment, and power flow analysis was done using the PANDAPOWER tool. The acquired results are contrasted using several contemporary optimization approaches to validate the suggested technique’s validity. The ARGA technique gives a lower congestion cost solution than the particle swarm optimization (PSO), real coded genetic algorithm (RCGA), and differential evolution (DE) algorithm.

Distributionally robust day-ahead scheduling for power-traffic network under a potential game framework

Widespread utilization of electric vehicles (EVs) incurs more uncertainties and impacts on the scheduling of the power-transportation coupled network. This paper investigates optimal power scheduling for a power-transportation coupled network in the day-ahead energy market considering multiple uncertainties related to photovoltaic (PV) generation and the traffic demand of vehicles. The crux of this problem is to model the coupling relation between the two networks in the day-ahead scheduling stage and consider the intra-day spatial uncertainties of the source and load. Meanwhile, the flexible load with a certain adjustment margin is introduced to ensure the balance of supply and demand of power nodes and consume the renewable energy better. Furthermore, we show the interactions between the power system and EV users from a potential game-theoretic perspective, where the uncertainties are characterized by an ambiguity set. In order to ensure the individual optimality of the two networks in a unified framework in day-ahead power scheduling, a two-stage distributionally robust centralized optimization model is established to carry out the equilibrium of power-transportation coupled network. On this basis, a combination of the duality theory and the Benders decomposition is developed to solve the distributionally robust optimization (DRO) model. Simulations demonstrate that the proposed approach can obtain individual optimal and less conservative strategies.

Stochastic adaptive robust approach for day-ahead energy market bidding strategies in hydro dominated sequential electricity markets

This paper presents a novel methodological approach for the optimal day-ahead energy market bidding behavior of a cascaded hydropower plants (HPPs) portfolio in the sequential electricity markets. The understudy markets are day-ahead energy market and manual frequency restoration reserve (mFRR) markets in both capacity and energy setups. The introduction of the mFRR capacity market ensures transmission system operators (TSOs) about the availability of energy bids in the real-time market, which acts as binding constraints in the mFRR energy markets. As a determining factor, the active-time duration of mFRR energy bids is uncertain at the time of day-ahead bidding, which is modeled as the intervals in our robust optimization, while the electricity prices are considered as the scenarios in the stochastic optimization. Hence, we have proposed a novel stochastic adaptive robust optimization to address the bidding problem in the face of uncertainties accurately. The results show a considerable improvement compared to the conventional fully-stochastic approach in the case study of Swedish cascaded hydropower plants.

A Partnership of Virtual Power Plant in Day-Ahead Energy and Reserve Markets Based on Linearized AC Network-Constrained Unit Commitment Model

This paper presents coordinated energy management as a virtual power plant (VPP) framework with a wind farm, a storage system, and a demand response program in the transmission network according to the cooperation of VPPs in day-ahead energy and reserve markets. This strategy is based on a bilevel method, where it maximizes the expected VPP revenue in the proposed markets subject to constraints of renewable and flexible sources and the VPP reserve model in the upper-level problem. Also, a market-clearing model based on network-constrained unit commitment (NCUC) is explained in the lower-level problem so that it minimizes the expected operating cost of generation units constrained to a linearized AC-NCUC model. The scenario-based stochastic programming (SBSP) models the uncertainties of loads and WF power generation. Then, the master/slave decomposition method solves the bilevel problem to achieve an optimal solution at a low computational time. Also, since the lower-level problem is mixed-integer linear programming, the Benders decomposition algorithm is adopted to solve this problem. Finally, the suggested approach is implemented on IEEE test networks in GAMS software, and numerical results confirm the efficiency of the coordinated VPP management in DA energy and reserve markets and its capabilities in improving network operation.