The ambitious CO2 emission targets of the Paris agreements are achievable only with renewable energy, CO2-free power generation, new policies, and planning. The main motivation of this paper is that future green fuels from power-to-X assets should be produced from power with the lowest possible emissions while still keeping the cost of electricity low. To this end we propose a power-to-X scheduling framework that is capable of co-optimizing CO2 emission intensity and electricity prices in the day-ahead electricity market scheduling. Three realistic models for local production units are developed for flexible dispatch and the impact on electricity market scheduling is examined. Furthermore, the possible benefits of using CO2 emission intensity and electricity prices trade-off in scheduling are discussed. We find that there is a non-linear trade-off between CO2 emission intensity and cost, favoring a weighted optimization between the two objectives.
The price of solar panels is decreasing rapidly and governments around the world are investing in solar power plants to increase the portion of solar energy in the smart grid. The price of electricity produced by solar power plants is much cheaper than conventional thermal power plants and innovation in solar cells is further increasing the divide. Consequently, setting up solar power plants will decrease electricity spot prices on average. However, the topology of the power grid must be considered while setting up solar power plants so that full benefits of cheap solar power can be gained by …
University of California San Diego（加州大学圣地亚哥分校）；San Diego State University（圣地亚哥州立大学）
In this paper, statistical machine learning algorithms, as well as deep neural networks, are used to predict the values of the price gap between day-ahead and real-time electricity markets. Several exogenous features are collected and impacts of these features are examined to capture the best relations between the features and the target variable. Ensemble learning algorithm namely the Random Forest issued to calculate the probability distribution of the predicted electricity prices for day-ahead and real-time markets. Long-Short-Term-Memory (LSTM) is utilized to capture long term dependencies…
The implementation of electricity markets based on locational marginal pricing in a multi-settlement process has allowed wholesale competition, with pricing mechanisms that incentivize the optimal allocation of generation, transmission, and demand-response resources. While efficiency and reliability gains have been achieved in the US at a regional level, the lack of adequate inter-regional coordination mechanisms has limited broader gains. The shortcomings of cross-border coordination of electricity markets become more apparent as the industry transitions towards higher penetration of renewabl…
House of Energy Markets and Finance（能源市场与金融研究院）；University of Duisburg-Essen（杜伊斯堡-埃森大学）
We present a winning method of the IEEE DataPort Competition on Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm. The day-ahead load forecasting approach is based on online forecast combination of multiple point prediction models. It contains four steps: i) data cleaning and preprocessing, ii) a holiday adjustment procedure, iii) training of individual forecasting models, iv) forecast combination by smoothed Bernstein Online Aggregation (BOA). The approach is flexible and can quickly adopt to new energy system situations as they occurred during and after COVID-19 shutdowns. The po…
Natural disasters require repairing all damaged components in electricity distribution networks. Optimal scheduling repair crews to minimize the aggregate duration of interruptions reduces the harm. We consider the fact that the number of switches is much smaller than the number of edges. The problem is modeled by a parallel identical machine scheduling with group soft precedence constraints to minimize the total weighted energization time. We propose an LP-based list scheduling algorithm and a conversion algorithm and analyze their theoretical performances.
In this paper, we propose a two-stage electricity market framework to explore the participation of distributed energy resources (DERs) in a day-ahead (DA) market and a real-time (RT) market. The objective is to determine the optimal bidding strategies of the aggregated DERs in the DA market and generate online incentive signals for DER-owners to optimize the social welfare taking into account network operational constraints. Distributionally robust optimization is used to explicitly incorporate data-based statistical information of renewable forecasts into the supply/demand decisions in the DA…
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 …
Optimizing production scheduling of steel plate hot rolling for economic load dispatch under time-of-use electricity pricing
Xiangtan University（湘潭大学）；Hunan Province Cooperative Innovation Center for Wind Power Equipment and Energy Conversion, College of Information Engineering, Xiangtan University（湘潭大学信息工程学院风力发电设备与能量转换湖南省协同创新中心）；Xiangtan Iron and Steel Corporation Ltd（湘潭钢铁集团有限公司）
As the demand for electricity and the need for power systems flexibility grow, it is crucial to exploit more reliable and clean sources of energy to produce electricity when needed most. Tidal lagoons generate renewable electricity by creating an artificial head difference between water levels on the seaside, driven by tides, and water levels inside the basin, controlled by flow through the structure. Depending on the level of seawater, power generation from a tidal lagoon can be controlled, i.e. shifting power generation in time. This paper aims to investigate the operation of a tidal lagoon …
This work presents a suite of two optimisation models for the short-term scheduling and redispatch of a virtual power plant (VPP) composed of a wind farm and a Li-ion battery, that participates in the day-ahead energy and secondary regulation reserve markets of the Iberian electricity market. First, a two-stage stochastic mixed-integer linear programming model is used to obtain the VPP's generation and reserve schedule and the opportunity cost of the energy stored in the battery. The model has an hourly resolution and a look-ahead period beyond the markets' scheduling horizon and considers the…
Optimal day-ahead scheduling for a system-centric community energy management system (CEMS) is proposed to provide economic benefits and user comfort of energy management at the community level. Our proposed community includes different types of homes and allows prosumers to trade energy locally using mid-market rate pricing. A mathematical model of the community is constructed and the optimization problem of this model is transformed into an MILP problem that can be solved in a short time. By solving this MILP problem, the optimization of the overall energy cost of the community and satisfact…
Tie-line scheduling in multi-area power systems in the US largely proceeds through a market-based mechanism called Coordinated Transaction Scheduling (CTS). We analyze this market mechanism through a game-theoretic lens. Our analysis characterizes the effect of market liquidity, market participants' forecasts about inter-area price spreads, transactions fees and coupling of CTS markets with up-to-congestion virtual transactions. Using real data, we empirically verify that CTS bidders can employ simple learning algorithms to discover Nash equilibria that support the conclusions drawn from equil…
Power systems are shifted from conventional bulk generation toward renewable generation. This trend leads to the frequency security problem due to the decline of system inertia. On the other hand, natural gas-fired units are frequently scheduled to provide operational flexibility due to their fast adjustment ability. The interdependence between power and natural gas systems is thus intensified. In this paper, we study the frequency constrained scheduling problem from the perspective of an integrated electricity-gas system under variable wind power. We propose a distributionally robust (DR) cha…
Grid-aware Scheduling and Control of Electric Vehicle Charging Stations for Dispatching Active Distribution Networks. Part-I: Day-ahead and Numerical Validation
Energy storage technologies are key to improving grid flexibility in the presence of increasing amounts of intermittent renewable generation. We propose an insurance contract that suitably compensates energy storage systems for providing flexibility. Such a contract provides a wider range of market opportunities for these systems while also incentivizing higher renewable penetration in the grid. We consider a day-ahead market in which generators, including renewables and storage owners, bid to be scheduled for the next operating day. Due to production uncertainty, renewable generators may be u…
In recent years, renewable energy resources have accounted for an increasing share of electricity energy.Among them, photovoltaic (PV) power generation has received broad attention due to its economic and environmental benefits.Accurate PV generation forecasts can reduce power dispatch from the grid, thus increasing the supplier's profit in the day-ahead electricity market.The power system of a PV site is affected by solar radiation, PV plant properties and meteorological factors, resulting in uncertainty in its power output.This study used multiple seasonal-trend decomposition using LOESS (MS…
Wireless charging technology provides a solution to the insufficient battery life of electric vehicles (EVs). However, the conflict of interests between wireless charging lanes (WCLs) and EVs is difficult to resolve. In the day-ahead electricity market, considering the revenue of WCLs caused by the deviation between actual electricity sales and pre-purchased electricity, as well as endurance and traveling experience of EVs, this paper proposes a charging scheduling algorithm based on a double-layer game model. In lower layer, the potential game is used to model the multi-vehicle game of vehicl…
We consider the scenario where $N$ utilities strategically bid for electricity in the day-ahead market and balance the mismatch between the committed supply and actual demand in the real-time market, with uncertainty in demand and local renewable generation in consideration. We model the interactions among utilities as a non-cooperative game, in which each utility aims at minimizing its per-unit electricity cost. We investigate utilities' optimal bidding strategies and show that all utilities bidding according to (net load) prediction is a unique pure strategy Nash Equilibrium with two salient…
Mobile energy storage devices (MESDs) operate as medium- or large-sized batteries that can be loaded onto electric trucks and connected to charging stations to provide various ancillary services for distribution grids. This paper proposes a new strategy for MESD operation, in which their power outputs and paths are co-optimally scheduled to minimize the total energy loss in both power and transportation networks. The distances moved by MESDs and time at different locations are modeled using a set of linear equations, considering the time-varying traffic flow. The linear transit model is integr…
A high number of electric vehicles (EVs) in the transportation sector necessitates an advanced scheduling framework for e-mobility ecosystem operation as a whole in order to overcome range anxiety and create a viable business model for charging stations (CSs). The framework must account for the stochastic nature of all stakeholders' operations, including EV drivers, CSs, and retailers and their mutual interactions. In this paper, a three-layer joint distributionally robust chance-constrained (DRCC) model is proposed to plan grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operation in day-ahead…
The interdependency of power systems and natural gas systems is being reinforced by the emerging power-to-gas facilities (PtGs), and the existing gas-fired generators. To jointly improve the efficiency and security under diverse uncertainties from renewable energy resources and load demands, it is essential to co-optimise these two energy systems for day-ahead market clearance. In this paper, a data-driven integrated electricity-gas system stochastic co-optimisation model is proposed. The model is accurately approximated by sequential mixed integer second-order cone programming, which can then…
