Variable Electricity Prices Research Articles

Planning level sizing of heat pumps and hot water tanks incorporating model predictive control and future electricity tariffs

Heat pumps and hot water tanks in local energy systems require sizing to increase on-site renewables self-consumption; decrease costs through variable electricity pricing; and utilise low-cost wind power. While detailed tools can capture these mechanisms, planning-level tools lack functionality and miss these benefits. In this paper an open-source planning-level modelling tool, PyLESA, is presented and applied to a sizing study to demonstrate the capturing of these benefits at the planning-level. Specific aims of the study were to investigate: (i) model predictive control vs. fixed order control, (ii) existing and future wind-influenced electricity tariffs, and (iii) optimal cost size combinations of heat pump and hot water tank. The lowest levelized cost of heat for the existing tariffs was for a time-of-use tariff, 750 kW heat pump and 500 m3 hot water tank combination. For the future wind-influenced tariff a 1000 kW heat pump and 2000 m3 hot water tank was cost optimal and showed model predictive control benefits over fixed order control with levelized heat costs reducing 41 %, and heat demand met by renewables increasing 18 %. These results demonstrate PyLESA as capable of capturing flexibility benefits at the planning stage of design and quantify the advantage of combining flexible tariffs with model predictive control.

Optimal lighting control in greenhouse by incorporating sunlight prediction

Supplemental lighting is an effective means for increasing greenhouse productivity. Recently, the use of light-emitting diodes (LEDs), capable of precise and quick dimmability, has increased in greenhouses. However, electricity cost of lighting can be significant, and hence, it is necessary to find optimal lighting strategies to minimize electrical lighting cost. In this paper, we model supplemental lighting in the greenhouses equipped with LEDs as a constrained optimization problem, and we aim to minimize electricity cost of supplemental lighting. We consider not only plant daily light integral (DLI) need during its photoperiod but also sunlight prediction and variable electricity pricing in our model. We use Markov chains to model sunlight irradiance and predict it during the day. By taking sunlight prediction information into account, we avoid supplying more light than the crop requires. Therefore, our lighting strategy prepares sufficient light for plant growth while minimizing electricity cost during the day. We propose an algorithm to find optimal supplemental lighting and evaluate its performance through exhaustive simulation studies using a whole year of weather data and compare it to a heuristic method, which aims to supply a fixed photosynthetic photon flux density (PPFD) to plants at each time step during the day. In addition to simulation studies, we also implemented the proposed lighting strategy in a research greenhouse in Athens, GA. Our prediction-based lighting approach shows (on average) over 45% electricity cost reduction compared to the heuristic method throughout the entire year.

ENERGY STAR Appliance Market Shares: Do They Respond to Electricity Prices, and Does It Matter?

We address an apparent paradox that the market shares of four ENERGY STAR appliances in the United States do not respond to within-state changes in electricity prices. We resolve the paradox by showing that market shares do respond to between-state variation in electricity prices. We also suggest an economic explanation for the paradox through the timing of appliance purchases. Using the estimation results, we find that the four ENERGY STAR appliances reduce carbon emissions by 1.9 million megawatt-hours per year, equivalent to removing 0.1% of all U.S. vehicles, and that the addition of a $100/ton CO2 price would increase these figures to 2.1 million megawatt-hours and 0.11%.

Energy Management System for Hybrid Microgrids

Energy management in grid-connected Micro-grids (MG) has undergone rapid evolution in recent times due to several factors such as environmental issues, increasing energy demand and the opening of the electricity market. The Energy Management System (EMS) allows the optimal scheduling of energy resources and energy storage systems in MG in order to maintain the balance between supply and demand at low cost. The aim is to minimize peaks and fluctuations in the load and production profile on the one hand, and, on the other hand, to make the most of renewable energy sources and energy exchanges with the utility grid. In this paper, our attention has been focused on a Rule-based energy management system (RB EMS) applied to a residential multi-source grid-connected MG. A Microgrid model has been implemented that combines distributed energy sources (PV, WT, BESS), a number of EVs equipped with the Vehicle to Grid technology (V2G) and variable load. Different operational scenarios were developed to see the behaviour of the implemented management system during the day, including the random demand profile of EV users, the variation in load and production, grid electricity price variation. The simulation results presented in this paper demonstrate the efficacy of the suggested EMS and confirm the strategy's feasibility as well as its ability to properly share power among different sources, loads and vehicles by obeying constraints on each element.

Investing in generation and storage capacity in a liberalised electricity market: An agent based approach

The power sector is undergoing a period of significant change, in terms of the mix of generation technologies, as well as the structure of energy markets, regulation and assets ownership. The shift to a more liberalised electricity system has resulted in an increase in the number of decision makers. This paper demonstrates an agent-based approach for investigating the long-term investment in the GB power generation sector, whilst considering the operability of the system. A key focus of this study is to investigate the efficacy of a range of policies to reduce the emissions and facilitate investment in renewable generation and battery storage. In order to capture the value of battery storage, the hourly operation of the electricity system, considering short-term variation of demand, renewable generation and wholesale electricity prices (including negative prices during high renewable and low demand events), was incorporated in the long-term investment model. The modelling results show while the cost of battery storage is expected to decrease gradually in future, a substantial subsidy is still required to justify investment in battery storage. The deployment of battery storage provides a significant reduction in the overall power generation system cost, peak demand and carbon emissions.

Techno-economic evaluation and optimization of CCGT power Plant: A multi-criteria decision support system

A key objective of the power generation industry is to achieve maximum economic benefit without over-consuming the life of power plants and over-maintaining its assets. From a CCGT power plant operator’s perspective, the stand-alone performance analysis of the plant is not enough to support the decision-making process due to the plethora of possible scenarios characterized by variable ambient conditions, engine health (fouling, erosion), electricity prices, and power demand. This study proposes a novel methodology to support decision-making for a CCGT power plant’s operational optimization. The comprehensive techno-economic performance evaluation is conducted by multidisciplinary optimization and decision-making to enhance information integration for the combined cycle power plant operated by Manx Utilities in the Isle of Man, UK. The decision support system has the capability to recommend the optimal operation schedules to plant operators. It recommends that the more severely degraded engine should run at a relatively lower power setting to decrease creep life consumption. The established power plant optimization framework has the potential to assist power plant operators in deciding the total power output and power split between gas turbines based on optimization results that considers both immediate thermo-economic benefits and life consumption. Finally, the proposed system can facilitate similar power plants to adjust daily operations to achieve thermo-economic and lifing benefits.

Conditionally independent increment processes for modeling electricity prices with regard to renewable power generation

Renewable energies have become responsible for a large part of the variation in electricity prices. We offer a new approach that makes it possible to represent this dependency in a flexible way. This allows to vary especially price spikes both in direction and size according to the generation of wind and solar power. The proposed concept is based on the construction of a process with conditionally independent increment. We provide an estimation procedure for them and offer a test to verify the dependency on an external variable. Based on the theoretical results, we fit an electricity price model for the German intraday market and use it to analyze power cap/floor prices.

Demand Response as a Real-Time, Physical Hedge for Retail Electricity Providers: The Electric Reliability Council of Texas Market Case Study

Residential demand response (DR) programs are generally administered through an electricity distribution utility, or an electric grid operator. These programs typically reduce electricity consumption by inducing behavioral changes in the occupants of participating households. We propose implementing a wholesale-price-sensitive residential DR program through the retail electricity provider (REP), who has more naturally aligned incentives to avoid high wholesale electricity prices and maintain customer satisfaction, as compared to distribution utilities, grid operators, and the average residential consumer. Retail electricity providers who serve residential consumers are exposed to substantial price risk as they generally have a portion of their portfolio exposed to variable real-time wholesale electricity prices, despite charging their residential customers a fixed retail electricity price. Using Monte Carlo simulations, we demonstrate that demand response, executed through internet-connected thermostats, to shift real-time residential HVAC load in response to real-time prices, can be used as an effective physical hedge, which is both less costly and more effective than relying solely on financial hedging mechanisms. We find that on average a REP can avoid USD 62.07 annually per household using a load-shifting program. Given that REPs operate in a low margin industry, an annual avoided cost of this magnitude is not trivial.

Only Extreme Fluctuations in Light Levels Reduce Lettuce Growth Under Sole Source Lighting.

The cost of providing lighting in greenhouses and plant factories can be high. In the case of variable electricity prices, providing most of the light when electricity prices are low can reduce costs. However, it is not clear how plants respond to the resulting fluctuating light levels. We hypothesized that plants that receive a constant photosynthetic photon flux density (PPFD) will produce more biomass than those grown under fluctuating light levels. To understand potential growth reductions caused by fluctuating light levels, we quantified the effects of fluctuating PPFD on the photosynthetic physiology, morphology, and growth of ‘Little Gem’ and ‘Green Salad Bowl’ lettuce. Plants were grown in a growth chamber with dimmable white LED bars, alternating between high and low PPFDs every 15 min. The PPFDs were ∼400/0, 360/40, 320/80, 280/120, 240/160, and 200/200 μmol⋅m−2⋅s–1, with a photoperiod of 16 h and a DLI of ∼11.5 mol⋅m−2⋅day–1 in all treatments. CO2 was ∼800 μmol⋅mol–1. Plants in the 400/0 μmol⋅m−2⋅s–1 treatment had ∼69% lower An,30 (net assimilation averaged over 15 min at high and 15 min at low PPFD) than plants grown at a PPFD of 320/80 μmol⋅m−2⋅s–1 (or treatments with smaller PPFD fluctuations). The low An,30 in the 400/0, and to a lesser extent the 360/40 μmol⋅m−2⋅s–1 treatment was caused by low net assimilation at 360 and 400 μmol⋅m−2⋅s–1. Plants grown at 400/0 μmol⋅m−2⋅s–1 also had fewer leaves and lower chlorophyll content compared to those in other treatments. The four treatments with the smallest PPFD fluctuations produced plants with similar numbers of leaves, chlorophyll content, specific leaf area (SLA), dry mass, and leaf area. Chlorophyll content, An,30, and dry mass were positively correlated with each other. Our results show that lettuce tolerates a wide range of fluctuating PPFD without negative effects on growth and development. However, when fluctuations in PPFD are extreme (400/0 or 360/40 μmol⋅m−2⋅s–1), chlorophyll levels and An,30 are low, which can explain the low poor growth in these treatments. The ability of lettuce to tolerate a wide range of fluctuating light levels suggests that PPFD can be adjusted in response to variable electricity pricing.

Profit Maximization for EVSEs-Based Renewable Energy Sources in Smart Cities With Different Arrival Rate Scenarios

This paper proposes a profit maximization model for a Decentralized Electric Vehicle Supply Equipment (D-EVSE) equipped with a renewable energy sources system such as a solar energy system. We acknowledge a power connection to the central power grid when necessary. We allow EV to offer its surplus power as discharging power processes. We design a Decentralized Profit Maximization Algorithm (DPMA) to help D-EVSEs take profit from the electricity price variation during the day when selling or buying electricity respectively to EVs or from the grid or EVs as discharging processes. Finally, numerical simulations with MATLAB are conducted to prove the effectiveness of our proposed solution.

Peer-to-Peer Energy Trading in Smart Grid Through Blockchain: A Double Auction-Based Game Theoretic Approach

In a smart grid, each residential unit with renewable energy sources can trade energy with others for profit. Buyers with insufficient energy meet their demand by buying the required energy from other houses with surplus energy. However, they will not be willing to engage in the trade if it is not beneficial. With the aim of improving participants' profits and reducing the impacts on the grid, we study a peer-to-peer (P2P) energy trading system among prosumers using a double auction-based game theoretic approach, where the buyer adjusts the amount of energy to buy according to varying electricity price in order to maximize benefit, the auctioneer controls the game, and the seller does not participate in the game but finally achieves the maximum social welfare. The proposed method not only benefits the participants but also hides their information, such as their bids and asks, for privacy. We further study individual rationality and incentive compatibility properties in the proposed method's auction process at the game's unique Stackelberg equilibrium. For practical applicability, we implement our proposed energy trading system using blockchain technology to show the feasibility of real-time P2P trading. Finally, simulation results under different scenarios demonstrate the effectiveness of the proposed method.

Optimal design and operation of hybrid renewable energy system for drinking water treatment

The industrial process of drinking water treatment requires intensive energy, leading to high enterprise costs and abundant carbon emissions. To ensure cost-effective and low polluting power supply for the above process, this paper proposes to deploy the grid-connected hybrid renewable system. A multi-objective nonlinear dynamic model mixed with integer variables is established for the decision makers, in which both system configurations and operations as well as benefits trade-offs from four dimensions are simultaneously considered. The ε-constraint method and system planner attitude parameters are introduced to transform the proposed model into its equivalent single objective form, which is further been solved by the LINGO software. A case study in China is given to assess the viability of the proposed model, in which optimal system configuration, economically feasible self-sufficiency ratio and optimal energy balance are obtained. Influences of electricity pricing strategies and natural resource changes on the systems are also analyzed and compared. It is found that the deployment of grid-connected hybrid wind/PV/storage system can help power users to cope with the future electricity price variation risks, with the feasible self-sufficiency ratio reaching 95%.

Coordinated Optimal Energy Management and Voyage Scheduling for All-Electric Ships Based on Predicted Shore-Side Electricity Price

Unlike a land-based standalone microgrid, a shipboard microgrid of an all-electric ship (AES) needs to shut down generators during berthing at the port for exanimation and maintenance. Therefore, the cost of onshore power plays an important role in an economic operation for AESs. In order to fully exploit its potential, a two-stage joint scheduling model is proposed to optimally coordinate the power generation and voyage scheduling of an AES. Different from previous studies that only consider the operation cost of the ship itself, a novel coordinated framework is developed in this article to address the shore-side electricity price variations on the ship navigation route. A deep learning-based forecasting method is utilized to predict the electricity price in various harbors for ship operators. Then, a hybrid optimization algorithm is designed to solve the proposed multiobjective joint scheduling problem. A navigation route in Australia is adopted for case studies and simulation results demonstrate the high energy utilization efficiency of the proposed algorithm and the necessity of on-shore power influence on the AES voyage.

A Coordinated Performance of Power System Operated with Participants in Demand Response Programs Considering Environmental Pollution Constraints

In a re-structured environment, demand response (DR) programs are presented in a new style. The demand response program consists of demand-side management methods which answers to electricity price variations. With advent of electricity markets, the demand-side management programs was introduced into two categories: (1) energy efficiency program, (2) demand response program. This paper studies engagement of participants load in an electricity market having a grid with large and small loads interested to participate in the market, energy storage systems, micro-grids, and distributed generations. In the proposed scheme, it is supposed to have an aggregator which sends demand-side preferences including load curtailment, load shifting, onsite generation, and energy storage systems along with proposed value and prices and all respective system constraints to the independent system operator (ISO). In fact, load aggregators submit aggregated DR offers to the ISO in order to optimize final decisions on aggregators’ DR contributions in wholesale market. The main goal of this paper is to solve the operation problem considering demand-side, system, and pollution and reliability constraints. Compared to other methods, the results indicate that the explicit modeling of customer DR would provide ISOs with more flexible options for scheduling the available energy resources in day-ahead energy markets.

Economics of seasonal photovoltaic soiling and cleaning optimization scenarios

The present study analyzes the soiling losses of a 1 MW photovoltaic system installed in the South of Spain. Both the Levelized Cost of Energy and the Net Present Value are used to compare the convenience of different mitigation strategies. It is found that also photovoltaic installations located in moderate regions, where the yearly soiling losses are limited to 3%, can suffer of a severe seasonal soiling, with power drops higher than 20%. In these conditions, an optimized cleaning schedule can be considerably beneficial from an economic perspective. For the given site, an optimal cleaning schedule generates a raise in profits up to 3.6% if one yearly cleaning is performed within a ±31-day window in summer. The convenience of one and multiple cleaning strategies is investigated by considering variable electricity prices and cleaning costs. In addition, the impact of the module efficiency on the cleaning strategy is analyzed. It is found that an optimized cleaning schedule can enhance the benefits of installing high efficiency modules, as it increases the amount of energy recovered through each cleaning and, therefore, the profits.

Is it time to launch grid parity in the Chinese solar photovoltaic industry? Evidence from 335 cities

This study evaluates grid parity in the Chinese solar PV industry by calculating the unsubsidized unit profits (UUPs) of solar PV projects in 335 Chinese cities. Furthermore, the effects of technological advances and various electricity price mechanisms on grid parity are explored. We consider the differences between centralized grid-connected and distributed projects. Two-factor learning curves are used to forecast technological progress and two kinds of electricity price mechanisms—government-regulated and market-based—are also brought into analyses. The results indicate the infeasibility of implementing complete grid parity regardless of the type of project. However, centralized grid-connected projects in 5 cities and distributed projects in 129 cities could implement pilot grid parity. On the whole, centralized grid-connected project has a lower benefit performance than distributed projects. Resource Zone I, which consists of Ningxia Province and several western cities in Gansu, Xinjiang and Inner Mongolia, has the best benefit performance of three resource areas. Technological advances and variations in electricity prices under both mechanisms could improve the UUPs but raising them above zero in all cities would still be impossible. The market prices of electricity under market-based mechanisms may generally have greater impacts than do government-regulated mechanisms on the benefit performances throughout the whole nation.

Price elasticities of residential electricity demand: Estimates from household panel data in Bangladesh

Abstract We collect a household level panel dataset to estimate the price elasticities of electricity demand for different types of urban households in Bangladesh. We use an instrumental variable estimation strategy which exploits exogenous variation in average electricity prices induced by a value-added-tax shock. The results indicate significant heterogeneity in price elasticities by electricity consumption levels. We conduct a number of simulations under alternative policy scenarios to illustrate how incorporating the heterogeneous nature of price elasticities into pricing policy can help decrease electricity demand-supply mismatch and inequality in electricity consumption. The results have important policy implications for developing countries aiming to address major energy issues by implementing tariff reforms.

Evaluation of the Administrative Phase-Out of Coal Power Plants on the Italian Electricity Market

Although decarbonisation is one of the most important macro-trends of this century, electricity generation from coal power plants is still broadly common. The main goal of this study is to evaluate the impact of a premature coal power plants phase-out on the Italian day-ahead electricity market. For this purpose, two electricity price forecasts, related to different scenarios between 2019 and 2030, and two different hypotheses for the creation of electricity spot price, were compared. The results from the different scenarios show that coal power plants phase-out determines a small variation in electricity price when bid-up is not considered; instead, when operators’ bid-up is included in the study, the price variation becomes relevant.

Modulating thermal load through lightweight residential building walls using thermal energy storage and controlled precooling strategy

Precooling is a recognized technique for reducing cooling energy in buildings during peak hours by shifting load to off-peak hours. This technique is particularly effective in buildings with high thermal mass, because of their large thermal energy storage capacity, and in commercial buildings due to their variable electricity pricing based on time-of-use rates. Precooling in residential buildings has been a matter of limited interest in the past because of their low thermal mass and typically uniform electricity pricing rate. While previous studies on precooling primarily focused on cost savings, an important aspect of precooling is the thermal load modulation, which could be very effective in managing peak demand in lightweight residential buildings integrated with thermal energy storage systems. In this study, we examine different precooling strategies to manage the heat gains in lightweight building walls integrated with phase-change materials. We create nine different precooling profiles by controlling the interior temperature, and then evaluate the influence of the precooling profiles on four key building energy performance parameters: total heat gain, peak heat gain, maximum heat gain during peak hours, and time at which peak occurs. To thoroughly understand the fundamental physics, we first consider hypothetical climates and obtain the optimal precooling strategy required to achieve maximum peak shedding and shifting while minimizing the total heat gains. We then extend the model to Baltimore, Maryland, and estimate the benefits of the optimized precooling strategy under real conditions. The optimal precooling strategy proposed in this study can shift the peak heat gain by up to 14 h, thereby reducing the heat gain during peak period by up to 95%, at the expense of 23% increase in the total heat gains.

Influence of thermal energy storage and heat pump parametrization for demand-side-management in a nearly-zero-energy-building using model predictive control

The rollout of the supply dependent generators wind turbines and photovoltaics leads to a flexibility demand that can be adressed from the consumer side, via Demand-Side-Management, as well. In single family houses, the heat pump, in combination with thermal energy storage, can shift their energy comsumption according to price signals in order to reduce consumer costs. This paper analyses the impact of different heat storage sizes and heat pump powers on cost savings and shifting potential, focussing on the Luxembourgish context, when variable electricity prices based on the electricity market are applied. A model predictive controller determines the cost-optimal operating cycles of the heat pump. The building’s heat demand is predicted with the help of a neural network. The results of the parametric study show significant differences in energy efficiency and cost savings. Furthermore limitations of taking advantage of variable electicity prices due to the price structure are disclosed. The cost savings however do not give a sufficient incentive for the consumer to invest in optimizing the heating system for Demand-Side-Management purposes. By consequence, the potential and the efficiency of Demand-Side-Management are limited and further incentives are necessary.