System Marginal Price Research Articles

Consideration of a Combined Cycle Gas Turbine’s Operating Modes in Pricing Mechanisms: A Korean Electricity Market Study

This study proposes a modeling technique for applying combined cycle gas turbines (CCGTs) to the unit commitment problem. The proposed method does not specify the turbine operation order in advance, and uses a simple and basic formula structured as mixed integer linear programming (MILP). In addition, we propose a system marginal price (SMP) calculation considering combination modeling of CCGTs, and we analyze market price change and its effect. After applying the proposed method to the unit commitment problem and calculating the market price from January 2017, although average market price and volatility increased, there was no rapid change, such as inversion of the dispatching rank of fuel sources or excessive fuel costs. And the profitability of the CCGTs participating in the day-ahead energy market improve.

Electricity autoproduction, storage and billing: A case study at Dundalk Institute of Technology, Ireland

Dundalk Institute of Technology (DkIT) is a third-level college in the east of Ireland with approximately 5000 registered students. The college is an autoproducer with an 850-kW wind turbine in operation on campus. In this study, DkIT’s half-hourly electricity usage over the 2017/18 academic year was measured and the benefit of the wind turbine was assessed in terms of energy, cost and carbon savings. In the period studied, the college imported electricity at fixed time-of-use (TOU) rates, although an alternative billing approach that was analysed is half-hourly pricing (HHP) based on the system marginal price (SMP). Furthermore, after a sizing study, the operation of a large Li-ion battery to be used for time-shifting was modelled. Over the study year, the wind turbine was found to reduce the college’s required import of electricity substantially, while also creating some export. This decreased the net electricity cost and net carbon emissions considerably. Irrespective of this, switching to HHP would also have provided further substantial economic savings. Conversely, the modelled time-shifting using the battery generated far too little revenue to cover the associated costs, although reserving the battery for use in the Irish grid-response markets appears to be an economically viable option.

Economic benefits of Smart Microgrids with penetration of DER and mCHP units for non-interconnected islands

The coordinated control of Distributed Energy Resources (DER) allows the full exploitation of DER advantages. Smart Microgrids (SM) seem to be the best solution for the management of modern Low Voltage (LV) grids with DER especially for Non-Interconnected Greek Islands. The main purpose of this paper is to investigate the economic benefits that can be obtained from the coordinated control of DER and micro-Combined Heat and Power (mCHP) Units in SM operation. For each examined scenario, various market prices expressed by the System Marginal Prices (SMP) and DER-mCHP penetrations are considered for annual operation. All different cases are compared between them to determine which one is the most advantageous in terms of operational cost. A typical LV SM, standard DER units and typical market values have been considered to provide initial results. In addition, a sensitivity analysis is carried out in order to determine the effect of installed DER power on grid operation, such as in operating cost, power losses, power injected from the upstream distribution network, local DER production, etc. The presented results show clear the economic benefits in SM operation.

Implementation of Optimal Scheduling Algorithm for Multi-Functional Battery Energy Storage System

Energy storage system (ESS) can play a positive role in the power system due to its ability to store, charge and discharge energy. Additionally, it can be installed in various capacities, so it can be used in the transmission and distribution system and even at home. In this paper, the proposed algorithm for economic optimal scheduling of ESS linked to transmission systems in the Korean electricity market is proposed and incorporated into the BESS (battery energy storage system) demonstration test center. The proposed algorithm considers the energy arbitrage operation through SMP (system marginal price) and operation considering the REC (renewable energy certification) weight of the connected wind farm and frequency regulation service. In addition, the proposed algorithm was developed so that the SOC (state-of-charge) of the ESS could be separated into two virtual SOCs to participate in different markets and generate revenue. The proposed algorithm was simulated and verified through Matlab and loaded into the demonstration system using the Matlab “Runtime” function.

Day-Ahead System Marginal Price Forecasting Using Artificial Neural Network and Similar-Days Information

Day-ahead system marginal price (SMP) forecasting constitutes essential information in the competitive energy market. Hence, this paper presents the development of a day-ahead SMP forecasting model via implementing an artificial neural network (ANN) algorithm. The accuracy of the ANN-based model is improved by including long-term historical data in addition to short-term historical data and by applying the k-fold cross-validation optimization algorithm. The selection of the short-term type input variable applies the Pearson correlation coefficient. Whereas the long-term type input variable is selected by applying the discrete Frechet distance in conjunction with the information related to the season and type of the day to find the Similar-Days Index. In order to verify the model, the forecasted load and actual SMP for 15 years of historical data are used. The results indicate that the proposed model can forecast SMP with higher accuracy than the conventional forecasting model.

Effects of different charging and discharging strategies of electric vehicles under various pricing policies in a smart microgrid

Nowadays, the effects of the incorporation of electric vehicles (EVs) have to be taken into consideration with care, since it may result in unfeasible operations for the grids (especially for the Smart Microgrids). Smart microgrids (SMs) seem to be the best solution for the management of modern low voltage (LV) grids with distributed energy resources (DER). The main purpose of this paper is to investigate the effects of different EVs' charging and discharging strategies in a SM. With EVs and absence of DERs and therefore satisfaction of the full load from the upstream network, is considered as the base case. For each scenario, six different charging technologies are examined. In addition, three different pricing policies based on system marginal price (SMP) are considered. All different cases are compared between them to determine which one is the most advantageous in terms of operational cost.

Effects of different charging and discharging strategies of electric vehicles under various pricing policies in a smart microgrid

Nowadays, the effects of the incorporation of electric vehicles (EVs) have to be taken into consideration with care, since it may result in unfeasible operations for the grids (especially for the Smart Microgrids). Smart microgrids (SMs) seem to be the best solution for the management of modern low voltage (LV) grids with distributed energy resources (DER). The main purpose of this paper is to investigate the effects of different EVs' charging and discharging strategies in a SM. With EVs and absence of DERs and therefore satisfaction of the full load from the upstream network, is considered as the base case. For each scenario, six different charging technologies are examined. In addition, three different pricing policies based on system marginal price (SMP) are considered. All different cases are compared between them to determine which one is the most advantageous in terms of operational cost.

Delivering electricity market insights in Northern Italy

This paper assesses the robustness and predictive power of a model comprising a complete set of variables affecting the electricity prices in Northern Italy. The Italian market is based on an implicit auction mechanism via six zonal prices, whose weighted average derives the System Marginal Price, or Prezzo Unico Nazionale (PUN). Given the focus on Northern Italy, as the country’s industrial core and main demand center, this work considers an exhaustive set of exogenous variables affecting a specific market zone, i.e. the North Zone. The import from bordering countries and market zones, the impact of non-programmable renewables, the load factor, the weather data and the prices of underlying commodities have been included. The econometric modelling of an ARMAX process for North Zone prices results in an under performance compared to a standard ARMA, to be fine-tuned, with potential implications regarding the reliance on timely adjustments on market information at trading floor level.

A power system scheduling model with carbon intensity and ramping capacity constraints

The integration of European electricity markets aims at market coupling among interconnected power systems and the evolution of environmentally friendly technologies. This process is anticipated to utilize more efficiently the flexible generation and interconnections transmission capacity and provide environmental and economic benefits to final consumers. This paper presents a mixed integer linear programming model for the optimal scheduling of a power system (unit commitment problem) simulating the day-ahead electricity market. The model determines the optimal daily power generation mix, the electricity trade with neighboring countries, the evolution of the system's marginal price and the resulting environmental impact. The model incorporates CO2 emissions intensity constraints and introduces flexible ramping products, in addition to reserve requirements, aiming to identify their impacts on both operational and economic decisions. The model is applied on the Greek power system and its interconnections with neighboring power systems in Southeast Europe. The proposed approach can provide useful insights on the optimal generation and interconnections portfolio that meets the real electricity market operating needs of contemporary power systems with environmental and ramping capacity constraints.

A Probabilistic Economic/CO2eq Emissions Dispatch Model: Real Applications

The worldwide evolution of the electric systems requires, i. a.: a) fossil-fuel generators with carbon capture and b) clean technologies based on renewable energies. For this reason, dispatch centers are in constant search for solutions in order to improve decision-making that involve the generation matrix. Consequently, in this paper a probabilistic economic dispatch model is proposed. The proposed methodology considers uncertainties, affecting the short–term control, the emission factors and the load dispatching. Wind speed, solar radiation and power demand are treated as random variables. Unavailability factors are also taken into account. The solution strategy is based on the Monte Carlo method and a bi-objective linear optimization constrained procedure. The approach involves multidimensional probabilities, descriptive statistics, clusters studies and bimodal analysis. The optimal solution yields the probability distributions of system marginal prices, dual costs, load-shedding, thermal and renewable power generation and emission factors. The proposed model and methodology are applied to the electric power system of northern Chile.

A mixed integer linear programming model to regulate the electricity sector

This paper presents a mixed-integer linear programming model for the optimal long-term electricity planning of the Greek wholesale generation system. In order to capture more accurately the technical characteristics of the problem, we have divided the Greek territory into a number of individual interacted networks (geographical zones). In the next stage we solve the system of equations and provide simulation results for the daily/hourly energy prices based on the different scenarios adopted. The empirical findings reveal an inverted-M shaped curve for electricity demand in Greece, while the system marginal price curve also follows a non-linear pattern. Lastly, given the simulations results, we provide the necessary policy implications for government officials, regulators and the rest of the marketers.

Consideration of Network Constraints in the Turkish Day Ahead Electricity Market

This paper proposes a new model for the Turkish Day Ahead Electricity Market including network equations. The existing one does not implicitly consider the power flow limitations, and these are considered separately that might result with a suboptimal day-ahead schedule. The dc network equations are added to the model, which is used to determine the Day Ahead System Sell and Buy Decisions and System Marginal Price in day-ahead planning. The impact of integration of network equations is exhibited using a small 6-bus network, mid-sized Turkish northwest sub-system, and full Turkish electric power system.

Carbon tax, system marginal price and environmental policies on Smart Microgrid operation

PurposeThe purpose of this paper is to optimally operate a Smart Microgrid which is interconnected to the main grid so as to minimize expenditures associated with CO2 emissions. Microgrids could come into play to aid the network through CO2 emission reduction while increasing their efficiency through local generation. For this purpose, a Smart Microgrid incorporating Distributed Energy Resources (DER), especially Renewable Energy Sources (RES), is operated optimally while keeping the CO2 emissions in check in order to minimize the financial burden from emissions stemming from the carbon tax. Since the network is assumed to be interconnected with the main grid, there is a consideration of the expected emissions associated with the imported energy.Design/methodology/approachAn economic/environmental dispatch problem is mathematically formulated using an objective function and the constraints that it is subject to. The methodology is applied on a typical 17-bus test distribution network, representing a Hellenic LV network. Various carbon tax rates and their impact on the system marginal price are examined, in terms of their effect on distributed generation (DG) and as a second step, the effect of imposing lower carbon tax rates for micro-sources with the goal of benefitting from their more eco-friendly generation capabilities. In order to assess that benefit, hourly grid emissions coefficients are derived based on actual grid data.FindingsThe CO2 tax refund policy towards the DG owners can lead to optimal coverage of consumers, optimal financial result both for the DG owners and the operator and greater DG integration within the smart grid.Originality/valueGreater DG integration within the smart grid by using a CO2 tax refund policy.

A genetic algorithm approach to the smart grid tariff design problem

Smart metering in electricity markets offers an opportunity to explore more diverse tariff structures. In this article residential electricity demand and the System Marginal Price of Ireland’s Single Electricity Market are simulated to estimate the wholesale risk associated with possible tariffs. A genetic algorithm (GA) with a stochastic fitness function is proposed to search for time-of-use tariffs that minimise wholesale risk to the supplier in residential markets. Alternative search algorithms and fitness functions are investigated in detail, as well as trade-offs in GA and simulation parameter settings.

Relaxation of quantitative energy objectives on generation expansion planning: A computational and policy study

Summary In the present research, a decision support tool is presented for the optimization of generation expansion planning (GEP) of semiliberalized electricity markets under specified quantitative energy objectives. The evolution of the anticipated System Marginal Price is estimated overtime, in conjunction with power sector's structure. A hybrid solver has been used for that task. It comprises of the Improved Stochastic Ranking Evolution Strategy (ISRES) and the Interior Point Algorithm (IPA), in an attempt to exploit their distinct characteristics. The quantitative energy objectives are represented as constraints. A relaxation factor is applied on the aforementioned constraints and its effect on the power sector's formation and the optimization procedure is demonstrated through a series of representative computational experiments. The results derived from using various relaxation levels are compared with a base case where the energy constraints are not embedded. Capacity orders are extracted denoting the optimal steps towards accomplishing the energy objectives. The evolution of some core indicators of the energy system is compared amongst the investigated cases. Relaxation facilitated the optimization procedure without intervening in GEP scheduling by much. Moreover, ISRES-IPA is benchmarked against commonly used solvers, demonstrating improved performance on the GEP model's optimization. The decision support tool presented and the conclusions extracted from this study may be of interest to energy policy makers, planners, and investors.

Wind, storage, interconnection and the cost of electricity generation

We evaluate how increasing wind generation affects wholesale electricity prices, balancing payments and the cost of subsidies using the Irish Single Electricity Market (SEM) as a test system, with hourly data from 1 January 2008 to 28 August 2012.We model the spot market using a system of seemingly unrelated regressions (SUR) where the regressions are the 24hours of the day. Wind has a negative impact on the system marginal price, with every MWh increase in wind generation (equal to about 0.2% of the average wind generation in our sample) leading to a decrease of the system marginal price of €0.018MWh, or about 0.3% of its average value.We use time series models to analyse the balancing market and show that wind generation increases balancing payments, as do the forecast errors of demand and wind.Every MWh of additional wind generation is associated with an increase in constraint payments of €3.2, or about 0.01%.Lack of storage increases the impact of wind on balancing payments whereas the lack of interconnection has no effect. Overall, wind decreases costs through its effect on the electricity price more than it increases constraint payments, even when storage is on outage. The effect of wind remains positive after including the cost of subsidies given to wind generation.

Detecting the impact of fundamentals and regulatory reforms on the Greek wholesale electricity market using a SARMAX/GARCH model

Abstract This work aims to detect the impact of fundamentals and regulatory reforms on the Greek Wholesale Electricity Market, applying SARMAX/GARCH models. The System Marginal Price (SMP) is considered a stochastic, nonlinear process, reflecting not only the effects of endogenous/fundamental market factors but also the effects of exogenous variables including regulatory reforms, which also affect the market dynamics. To capture the dynamics of the conditional mean and volatility of SMP, a number of SARMAX/GARCH models have been estimated using as regressors an extensive set of fundamental factors in the Greek Electricity Market (GEM), as well as dummy variables that mimic the history of Regulator's interventions. The best-found model captures adequately the dependency of the spot price to the regulatory reforms. The findings reassure the typical sign and the magnitude of the effect of fundamentals, and detects successfully the impacts of the reforms. The most interesting finding is that the GEM does not exhibit asymmetries or leverage effect, in the volatility of its wholesale price, as the most European markets do. The outcome of this paper can be useful to a wide variety of GEM's participants and specifically to the decision makers in GEM.

Economic benefits from the coordinated control of Distributed Energy Resources and different Charging Technologies of Electric Vehicles in a Smart Microgrid

In the near future, Electric Vehicles (EVs’) penetration is expected to rise, making the study of their local level effects imperative. EVs is a new, promising, technology which will offer cleaner and more effective means of transportation. On the other hand, EVs can also act as storage devices, thus offering ancillary services to the grid. The optimum operation of Low Voltage (LV) grids, with special emphasis on Smart Microgrids (SM), must be ensured and the financial viability of EVs’ penetration is to be assessed. Smart Microgrids seem to be the best solution for the management of modern LV grids with Distributed Energy Resources (DER). The main purpose of this paper is to investigate the economic benefits that can arise from the coordinated control of DER and EVs in SM operation. Absence of distributed resources, and therefore satisfaction of the full load from the upstream network, is considered as the base case. Various pricing policies based on System Marginal Prices (SMPs) are considered. For each scenario, three different charging technologies are examined. All different cases are compared between them to determine which one is the most advantageous in terms of operational cost.

Spatial Effects in the Bid Price Setting Strategies of the Wholesale Electricity Markets: The Case of Colombia

Weather conditions in Colombia vary greatly throughout the territory and therefore the location of electricity generating plants plays a key role in their bid pricing strategies. To account for these location-specific pricing strategies this paper estimates a Spatial Durbin Model (SDM) with monthly data gathered from the 17th largest hydraulic electricity generating plants of Colombia on bid prices, generation, energy inputs and positive reconciliation over the period January 2005-August 2015 and controlling also for the system marginal prices and the economy cycle. The paper reports three main results. First, firms bid prices are negatively affected by the energy inputs of the rivals, second they are unaffected by positive reconciliation payments to the rivals and third they are negatively affected by the generation amounts of the rivals. One potential policy recommendation of these results is the need to implement balancing markets to signal more efficiently the pricing strategies in these markets.

Wind, Storage, Interconnection and the Cost of Electricity Generation

We evaluate how increasing wind generation affects wholesale electricity prices, balancing payments and the cost of subsidies using the Irish Single Electricity Market (SEM) as a test system, with hourly data from 1 January 2008 to 28 August 2012. We model the spot market using a system of seemingly unrelated regressions (SUR) where the regressions are the 24 hours of the day. Wind has a negative impact on the system marginal price, with every MWh increase in wind generation (equal to about 0.2% of the average wind generation in our sample) leading to a decrease of the system marginal price of €0.018/MWh, or about 0.3% of its average value. We use time series models to analyse the balancing market and show that wind generation increases balancing payments, as do the forecast errors of demand and wind. Every MWh of additional wind generation is associated with an increase in constraint payments of €3.2, or about 0.01%. Lack of storage increases the impact of wind on balancing payments whereas the lack of interconnection has no effect. Overall, wind decreases costs through its effect on the electricity price more than it increases constraint payments, even when storage is on outage. The effect of wind remains positive after including the cost of subsidies given to wind generation.