Deregulated Energy Market Research Articles

Clean energy utility for multifamily housing in a deregulated energy market

Energy efficiency and renewable energy (EERE) investment in multifamily residences in the United States has not kept pace with investment in resident-owned facilities. Split incentives, where owners cannot benefit economically from energy cost savings for residences and resident investment in EERE is not feasible, have posed a significant barrier. A clean energy utility is posited to circumvent this barrier. This utility would be responsible for power purchase from the grid, ideally as a real-time purchase agent from the grid manager; investment in energy efficiency and renewable energy; and demand management through control of water heating, as well as supply-side management through deployment of stored solar at near-peak grid power purchase cost. A clean energy fee is posed for recovery of costs, in contrast to typical consumption strategies (per kWh).A case study approach is employed to evaluate the feasibility of this type of utility of reducing carbon production in this building sector. Considered in the analysis is a 2008 multifamily facility located in the Midwest of the U.S., with apartment level interval meters for both power and water. Historical data from these meters were used to assess the savings and demand-side management potential from investments in improved efficiency lighting, refrigeration, heat pumps, and water heaters, as well as investments in solar PV and storage for supply-side management. The results show that the packaged retrofit EERE investment could yield costs for residents and profits for energy manager comparable to those in the current residential pricing scheme, while reducing grid-sourced energy by 42%. When solar PV and battery storage are added to the solution, it is shown that a clean energy fee structure can cost-effectively drive savings to over 54%. For new construction, even deeper cost effective savings are realizable. This research demonstrates the potential to drive deep energy savings in the multifamily building sector that can lower costs to residents through the establishment of clean energy utilities which recover investments in energy efficiency, demand management, and solar PV/battery systems through resident clean energy fees rather than consumption fees.

A multi-stage stochastic optimization model for energy systems planning and risk management

Strategic decision making at the building level is gaining importance in the context of a more and more deregulated energy market. The increase of the set of available options regarding distributed and renewable energy technologies leads to a complex decision process. Importantly, such decision making process is affected by uncertainties and therefore stochastic models are needed. In this paper, a comprehensive deterministic strategic optimization model for energy systems planning at the building level is extended to a stochastic optimization framework, thereby allowing the decision maker to manage risks in addition to considering the variability of the uncertain parameters. A numerical example shows the importance of taking into account uncertainty and risk in this kind of problems.

Important variable assessment and electricity price forecasting based on regression tree models: classification and regression trees, Bagging and Random Forests

Electricity price forecasting has become the focus of considerable interest in a deregulated energy market. In this study, regression tree-based models: classification and regression trees, Bagging and Random Forests have been built and used to identify the variables dominating the marginal price of the commodity as well as for short-term (one hour and day ahead) electricity price forecasting for the Spanish–Iberian market. Different prediction models are proposed including the main features of the market such as load, hydro and thermal generation and from available, wind energy production, of strategic interest in the Spanish market. In addition other explanatory variables are considered as lagged prices, as well as hour, day, month and year indicators. In the study, hourly data from 2000–2011 corresponding to 22 variables have been used. The results show the effectiveness of the proposed ensemble of tree-based models which emerge as an alternative and promising tool, competitive with other existing methods.

Dynamic behaviour analysis of a three pressure level heat recovery steam generator during transient operation

The context of the deregulated energy market and the rapid expansion of the non-predictable energy sources, are stressing the necessity of improving the flexibility of traditional power plants. Cyclic operation guarantees high profits in the short term but causes a lifetime reduction due to thermo-mechanical fatigue, creep and corrosion. In this context, combined cycle gas turbine power plants are the most concerned in flexible operation. However, the most stressed devices are in the Heat Recovery Steam Generator. For these reasons, in the present work, a procedure able to estimate the most stressed devices lifetime reduction is proposed. The core of the procedure is the power plant dynamic model. The selected test case is a 380 MW combined cycle gas turbine power plant composed by a gas turbine and a three pressure level Heat Recovery Steam Generator with reheat. The model's peculiarity is the full implementation of the components geometry. The model is able to predict the plant steady-state, part-load and dynamic behaviour. Different transient conditions are tested and the superheater lifetime reduction computed. Results show that the procedure can be considered as valuable innovative instrument to assist power plant designers and operators in order to improve the plant's flexibility.

Prediction of energy performance of residential buildings: A genetic programming approach

Energy consumption has long been emphasized as an important policy issue in today's economies. In particular, the energy efficiency of residential buildings is considered a top priority of a country's energy policy. The paper proposes a genetic programming-based framework for estimating the energy performance of residential buildings. The objective is to build a model able to predict the heating load and the cooling load of residential buildings. An accurate prediction of these parameters facilitates a better control of energy consumption and, moreover, it helps choosing the energy supplier that better fits the energy needs, which is considered an important issue in the deregulated energy market. The proposed framework blends a recently developed version of genetic programming with a local search method and linear scaling. The resulting system enables us to build a model that produces an accurate estimation of both considered parameters. Extensive simulations on 768 diverse residential buildings confirm the suitability of the proposed method in predicting heating load and cooling load. In particular, the proposed method is more accurate than the existing state-of-the-art techniques.

Harmonic Distortion Contribution for the Transmission Loss Allocation in Deregulated Energy Market: A New Scheme for Industry Consumer

The industry has rapidly growth and energy supply technology advanced are become main factor which to contribute of the harmonic losses. This problem is one aspect that may affect the capability of the transmission line and also to the efficiency of electricity. This paper proposes a new scheme to allocate the cost pertaining to transmission loss due to harmonics. The proposed method, called as Generalized Harmonic Distribution Factor, uses the principle of proportional sharing method to allocate the losses among the transmission users especially for industry consumers. The IEEE 14- and 30 bus test system is used to compare the proposed method with existing method. The results showed that the proposed method provided a scheme better in allocating the cost of transmission loss, which could encourage the users to minimize the losses.

Renewable energy integration in smart grids-multicriteria assessment using the fuzzy analytical hierarchy process

Unlike the traditional way of efficiency assessment of renewable energy sources integration, the smart grid concept is introducing new goals and objectives regarding increased use of renewable electricity sources, grid security, energy conservation, energy efficiency, and deregulated energy market. Possible benefits brought by renewable sources integration are evaluated by the degree of the approach to the ideal smart grid. In this paper, fuzzy analytical hierarchy process methodology for the integration efficiency has been proposed, taking into account the presence of multiple criteria of both qualitative and quantitative nature, different performance indicators, and the uncertain environment of the smart grid. The methodology has been illustrated on the choice of the size and location of a distributed generator in the radial distribution feeder.

Combined cycle power plants: A comparison between two different dynamic models to evaluate transient behaviour and residual life

The deregulated energy market and the increasing quota of electrical capacity covered by non-predictable renewable sources require strongly irregular and discontinuous operation of thermoelectric plants to satisfy users demand and compensate the variability of renewable sources. As a consequence, due to thermo-mechanical fatigue, creep and corrosion, a lifetime reduction of the most critical components occurs.The availability of a procedure able to predict the residual life of plant devices is necessary to assist the management decisions about power plants’ operation and maintenance scheduling. The first step of this procedure is the capability of simulating the plant behaviour versus time by evaluating the trends of the main thermodynamic parameters that describe the plant operation during different transient periods. In this context, the main contribution of the present paper is to propose a complete procedure able to simulate the plant dynamic behaviour and estimate the residual life reduction of some components. Indeed, two different models, developed by two different research groups, of the same single pressure heat recovery steam generator unit are presented and utilized to characterize the dynamic behaviour of the above mentioned power plant. The main thermodynamic variables during different transient operation conditions are predicted and good correspondence between the two methods is obtained. It can be also noted that, when the geometry and size of the devices are considered, the thermal inertia related to heat exchangers tubes, pipes and other physical masses causes a delay in the system response. Moreover, a residual life estimation of the most stressed component is presented.

A new generation of small hydro and pumped-hydro power plants: Advances and future challenges

Abstract Hydropower is not only a renewable and sustainable energy source, but its flexibility and storage capacity also makes it possible to improve grid stability and to support the deployment of other intermittent renewable energy sources such as wind and solar power. As a result, a renewed interest in pumped-hydro energy storage plants (PHES) and a huge demand for the rehabilitation of old small hydropower plants are emerging globally. As regards PHES, advances in turbine design are required to increase plant performance and flexibility and new strategies for optimizing storage capacity and for maximizing plant profitability in the deregulated energy market have to be developed. During the upgrading of old small hydropower plants, the main challenges to be faced are the design of new runners, that had to match the existing stationary parts, and the development of optimal sizing and management strategies to increase their economic appeal. This paper traces an overview of the prospects of pumped-hydro energy storage plants and small hydro power plants in the light of sustainable development. Advances and future challenges in both turbine design and plant planning and management are proposed. PHES and hybrid wind/solar-PHES are illustrated and discussed, as well as the limits and peculiarities of the new design strategies, based on computational fluid dynamics, for both PHES and small hydropower plants.

Application of efficient self-adaptive differential evolutionary algorithm for voltage stability analysis under practical security constraints

In the deregulated energy market, detection of weaker buses, ceiling points are essential to avoid possible voltage collapse for the preventive control actions and voltage security assessment. In this paper, unique self-adaptive differential evolutionary (SADE) technique is developed to detect the ceiling point (CP), weaker buses under practical security constraints. Considering the best, worst and current objective functions, both crossover rate (Cr) and mutation factor (F) of DE algorithm are so designed that those tuning parameters will be adaptive with the generation. The new mathematical modeling of the objective function is developed considering the actual security limitations. For both standard IEEE test systems and ill-conditioned systems, the performances of the proposed algorithm are compared with the other evolutionary techniques like ordinary differential evolution (ODE), general particle swarm optimization (GPSO), real coded genetic algorithm (RCGA). The best performances in terms of convergence rate, success rate, and ceiling point are achieved for the SADE algorithm because of the innovative formula of adaptive Cr and F. The FACTS devices are placed on the weaker buses. In this paper, the weaker buses are also detected. Showing characteristics and results, the robustness and efficiency of the proposed algorithm are established.

A New Two-Stage Approach to Short Term Electrical Load Forecasting

In the deregulated energy market, the accuracy of load forecasting has a significant effect on the planning and operational decision making of utility companies. Electric load is a random non-stationary process influenced by a number of factors which make it difficult to model. To achieve better forecasting accuracy, a wide variety of models have been proposed. These models are based on different mathematical methods and offer different features. This paper presents a new two-stage approach for short-term electrical load forecasting based on least-squares support vector machines. With the aim of improving forecasting accuracy, one more feature was added to the model feature set, the next day average load demand. As this feature is unknown for one day ahead, in the first stage, forecasting of the next day average load demand is done and then used in the model in the second stage for next day hourly load forecasting. The effectiveness of the presented model is shown on the real data of the ISO New England electricity market. The obtained results confirm the validity advantage of the proposed approach.

On the operation strategy of steam power plants working at variable load: Technical and economic issues

The context of the deregulated energy market leads to high competitiveness among producers and requires suitable strategies in plants and systems management: strongly irregular and discontinuous operation is required in order to meet the user demand and produce energy mainly during peak hours, when the electricity price is higher. This operation strategy is generally asked of all power plants, not only those traditionally devoted to load regulation and peak request, but also those originally designed to cover the base load (steam power plants, for example). As a consequence, greater income is ensured in the short term, but a reduction in the lifetime of the most critical components is likely to occur, due to creep and thermo-mechanical fatigue loadings. This will cause additional costs associated with unplanned maintenance and unavailability of the plant if a failure occurs. This paper presents a procedure aimed at evaluating this extra cost related to flexible operation, and at assisting the management decision about power plants’ operation and maintenance scheduling. The procedure, on the basis of the historical data, predicts the residual life of the most critical components, considering the effects of creep, thermo-mechanical fatigue, welding, corrosion and oxidation. It also permits one to choose different future strategies for plant management and evaluate the residual life and the economic effects for each of them. An example of application to a real steam power plant will also be presented.

Distribution system planning considering integration of distributed generation and load curtailment options in a competitive electricity market

Distribution systems management is becoming an increasingly complicated issue due to the introduction of new technologies, new energy trading strategies and new deregulated environment. In the new deregulated energy market and considering the incentives coming from the technical and economical fields, it is reasonable to consider distributed generation (DG) as a viable option to solve the lacking electric power supply problem. This paper presents a mathematical distribution system planning model considering three planning options to system expansion and to meet the load growth requirements with a reasonable price as well as the system power quality problems. DG is introduced as an attractive planning option with competition of voltage regulator devices and interruptible load. In mathematical model, the object function includes investment costs, which are evaluated as annualized total cost, plus total running cost as well as cost of curtailed loads and losses. This model identifies the optimal type, size and location of the planning options. In the studies presented in this paper also the fluctuation of load and electricity market price versus time period and the effect of DG placement on system improvement are considered. To solve the proposed mathematical planning model a new software package interfacing MATLAB and GAMS is developed. This package is enabling to solve large extent distribution system planning program visually and very fast. The proposed methodology is tested in the case of the well-known IEEE 30-bus test system.

Energy from wind – perspectives and research needs

Wind energy will provide a major share of our future energy supply, and, due to the possibility of going offshore, has an immense additional potential for power supply. To use wind energy in a reliable way, much research work still has to be done. One of these fields is the evaluation of wind energy in terms of sustainability. The results of Life Cycle Assessments (LCA) show that wind energy converters have a short energy payback time and low emissions, even in comparison with other renewable energy systems. Another field of research is the creation of optimisation models which can provide answers to questions concerning the future power generation mix. The opportunity exists, now that a great part of the over-aged conventional power plant stock has to be replaced, to adapt the power plant mix to an expanded share of wind energy. Within this mix, electric storage may play a major role. The results of the optimisation models indicate that storage sites alone will not be profi enough to reduce operating costs of the modelled system, and that this will only be possible through the option of energy trading. Hence there are interesting instruments in the deregulated energy market, especially for electric-power commodity exchanges like the EEX.

Measurements get together

This article deals with the synchronized measurement technology that has the potential of becoming the backbone for real-time monitoring system. The secure and reliable operation of modern power systems is an increasingly challenging task due to the ever-increasing demand for electricity, the growing number of interconnections, penetration of variable renewable energy sources, and deregulated energy market conditions. Power companies in different parts of the world are therefore feeling the need for a real-time wide area monitoring, protection, and control (WAMPAC) system. Synchronized measurement technology (SMT) has the potential of becoming the backbone of this system. The major advantages of using SMT are that 1) the measurements from widely dispersed locations can be synchronized with respect to a Global Positioning System (GPS) clock, 2) voltage phase angles can be measured directly, which was so far technically infeasible, and 3) the accuracy and speed of energy management system (EMS) applications increases manifold.

Oscillation Behaviour of the Enlarged UCTE Power System Including the Turkish Power System

Due to deregulated energy market conditions and the planned extension of the UCTE power system towards Eastern Europe, as well as towards the Middle East and North Africa to close the so called “Mediterranean Ring”, the oscillation damping behaviour of the UCTE power system is gaining more and more in importance. Within the present paper, the oscillation damping behaviour of the enlarged UCTE power system after the synchronous connection with the Turkish power system is analysed, using time and frequency domain methods. Firstly the Turkish power system is analysed as a separate network in isolated operation as the case still is today. Subsequently the enlarged UCTE-system, including the Turkish power system is analysed. Differences in the oscillation behaviours are shown, precarious system constellations are identified and measures to solve occurring problems are given and discussed.

Optimal Electrical Distribution Systems Reinforcement Planning Using Gas Micro Turbines by Dynamic Ant Colony Search Algorithm

Distribution systems management is becoming an increasingly complicated issue due to the introduction of new energy trading strategies and new technologies. In this paper, an optimal reinforcement strategy to provide reliable and economic service to customers in a given time frame is investigated. In the new deregulated energy market and considering the incentives coming from the political and economical fields, it is reasonable to consider distributed generation (DG) as a viable option for systems reinforcement. In the paper, the DG technology is considered as a possible solution for distribution systems capacity problems, along several years. Therefore, compound solutions comprising the installation of both feeders and substations reinforcement and DG integration at different times are considered in the formulation of a minimum cost distribution systems reinforcement strategy problem. An application on a medium size network, hypothesizing a scenario of reinforcement also using as DG gas micro-turbines, is carried out using a novel optimization technique allowing the identification of optimal paths in trees or graphs. The proposed technique is the Dynamic Ant Colony Search algorithm

The effect of Solar irradiance on the power quality behaviour of grid connected photovoltaic systems

Photovoltaic systems have been increasingly used in the generation of electrical energy, either as a means of providing electricity in areas where there is no grid connection (stand alone systems), or by providing electricity to the grid (grid connected systems). In a deregulated energy market, the cost of energy produced from fossil fuels is rising and the photovoltaic energy becomes a promising alternative source. Yet it still suffers from problems that need to be resolved in order to be widely accepted as an equivalent alternative to fossil fuels. Firstly the more direct problem of efficiency and cost needs to be overcome and secondly the indirect problem of the quality of power provided by photovoltaic technology has to be addressed. In this work 14 different grid connected PV systems have been considered and power quality measurements have been taken. Results for low and average irradiance cases are presented and analyzed in order to determine how power quality quantities are affected by changes in solar irradiance.

Load Following Control Schemes for Deregulated Energy Markets

Load following can be carried out in a centralized way by the independent system operator from scheduling coordinators in the day-ahead market, hour-ahead market and in real-time or by longer term contracts, or can be competitively procured through bilateral contracts between supplier and customer. In this paper, a new method has been developed for correlating system imbalances with unbalanced transactions in a bilateral transaction-based market. Depending on the assumed-even coexisting-market structure for load following service, two different control schemes are required to excite reset controllers to vary generator outputs until power imbalances vanish. The suggested procedure has been implemented on the IEEE 30-bus test system under a variety of operating conditions and power system disturbances, which confirm the effectiveness of the proposed methodology to selectively assign the load following burden to unbalanced transactions

Application of Q-learning with temperature variation for bidding strategies in market based power systems

The electric power industry is confronted with restructuring in which the operation scheduling is going to be decided based on a competitive market. In this new arrangement, bidding strategy has become a major issue. Participants in this deregulated energy market place may be able to compete better by choosing a suitable bidding strategy for trading electricity. Different classical methods for decision making in the uncertain environment of the market can be applied to select a suitable strategy. Most of these methods, such as game theory, that insure reaching the best solution for all market participants, require a lot of information about the other market players and the market. However, in the real market place only a little information, such as the spot price, is available for all participants. In this paper, a modified reinforcement learning based on temperature variation has been first proposed and then applied to determine the optimal strategy for a power supplier in the electricity market. A Pool-Co model has been considered here, and the simulation results are shown to be the same as those of standard game theory. Adaptation of the method in the presence of parameter variation has been verified as well. The main advantage of the proposed method is that no information about other participants is required. Furthermore, our investigation shows that even if all participants use this method, they will stay in Nash equilibrium.