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This study investigates the potential application of hybridized energy system (i.e., PV/Wind/Diesel) with battery storage in the northern region of Bangladesh. A techno-economic feasibility of different system configurations is evaluated and an optimized system is selected using HOMER (Hybrid Optimization Model for Electric Renewable) software. The hybrid system is optimized to meet a remote stand-alone village community’s load demand of 242.56 kWh/day with a 51.52 kW peak load demand. The result of this study indicates that the optimized hybrid system consists of 73 kW PV arrays, a 57 kW Diesel generator, a 387 kWh nominal capacity battery bank, and 28 kW inverters have the minimum Cost of Energy (COE) of 0.37$/kWh and the Net Present Cost (NPC) of $357,284. The outcome also indicates that the optimized system reduces CO 2 emission by around 62% in comparison with the kerosene used in a current situation and 67% with the grid connected system. Furthermore, the Life Cycle Emissions-LCE (kg CO 2 -eq/yr) production is considerably lower in PV/Batt/ICE system than the other system configurations. However, this system has a lower Duty Factor (DF) compared to Wind/Batt/Diesel and Batt/Diesel based system. Although this system is not comparable with the grid tariff, the proposed method is economically feasible than solar micro utility system, Wind/Batt/Diesel system, and Diesel generator only system. Additionally, the article discusses the social and economic benefits of implementing the hybridized system along with their barriers and challenges. [ABSTRACT FROM AUTHOR]

In the present study, an experimental setup for a PVT system is designed and fabricated to investigate the economic and environmental aspects of using different nano-oxides/water fluids. Selected coolant fluids are: pure water, ZnO/water, Al 2 O 3 /water and TiO 2 /water nanofluids. To make nanofluids, nanoparticles are dispersed in distilled water by 0.2% weight fractions (wt%), separately. Based on the experimental results the amount of annual emission reduction and cost saving are investigated for different coolants. Furthermore, the payback period of the PVTs is compared with that of a conventional PV unit. The results are presented in five different scenarios due to increasing of the electricity price, up to the final price and the government subsidies for the renewable energy sources (1st: 10% annual price increasing, with no subsidy, 2nd: linear increasing price, with no subsidy, 3rd: 10% the annual price increasing, 50% subsidy, 4th: linear price increasing, 50% subsidy and 5th: linear price increasing, 75% subsidy). From the energy viewpoint, by using pure water, PVT/TiO2, PVT/ZnO and PVT/Al 2 O 3 , the size reduction of the PVT system in comparison with that of the PV is 21, 32, 33 and 24%, respectively, and from the exergy viewpoint these values are about 5, 6, 7, 6%, respectively. [ABSTRACT FROM AUTHOR]

SUMMARY: In this paper, a hybrid model of fuzzy clustering and ANN (Artificial Neural Network) is proposed for electricity price forecasting. Due to the complicated behavior of electricity price in power markets, market players are interested in maximizing profits while minimizing risks. As a result, more accurate models are required to deal with electricity price forecasting. This paper proposes a new method that makes use of fuzzy clustering preconditioned GRBFN (Generalized Radial Basis Function Network) to provide more accurate predicted prices. Fuzzy clustering plays a key role to prevent the number of learning data from decreasing at each cluster. GRBFN is one of efficient ANNs to approximate nonlinear systems. Furthermore, a modified GRBFN model is developed to improve the performance of GRBFN with the use of DA (Deterministic Annealing) clustering for the parameters initialization and EPSO (Evolutionary Particle Swarm Optimization) for optimizing the parameters of GRBFN. The proposed method is successfully applied to real data of ISO New England, USA. [ABSTRACT FROM AUTHOR]

The robust generation self-scheduling problem under electricity price uncertainty is usually solved by the commercial solver, which is limited in computation time and memory requirement. This paper proposes an improved Lagrangian relaxation algorithm for the robust generation self-scheduling problem where the quadratic fuel cost and the time-dependent exponential startup cost are considered. By using the optimal duality theory, the robust generation self-scheduling problem, which has a max-min structure, is reformulated as a minimization mixed integer nonlinear programming (MINLP) problem. Upon the reformulation, the Lagrangian relaxation algorithm is developed. To obtain a solvable relaxed problem, the variable splitting technique is introduced before the relaxation. The obtained relaxed problem is decomposed into a linear programming-type subproblem and multiple single-unit subproblems. Each single-unit subproblem is solved optimally by a two-stage backward dynamic programming procedure. The special cases of the problem are discussed and a two-stage algorithm is proposed. The proposed algorithms are tested on test cases of different sizes and the numerical results show that the algorithms can find near-optimal solutions in a reasonable time. [ABSTRACT FROM AUTHOR]

By advancement and vogue of smart grid technologies, there is a strong attitude toward utilizing different strategies for participating in demand response (DR) programs in electricity markets. DR programs can be classified into two main categories namely incentive-based programs (IBPs) and time-based rate programs (TBRPs). In this paper, an improved incentive-based DR (IBDR) model is proposed. In our proposed IBP, the concept of elasticity is improved where it depends not only on the electricity price, but also is a function of consumption hour and customer type. In this program, the incentive value which is paid to the participating consumers is not a fix value and relates to the peak intensity of each hour. The proposed IBP can participate in both of day-ahead and intra-day electricity markets. The property of considering intra-day market enables consumers to provide maximum DR if possible. The proposed model is implemented on peak load curve of Spanish electricity market and a 200-unit residential complex. Different scenarios are considered to show effectiveness of the proposed DR model from various aspects including peak shaving as well as economic indices. [ABSTRACT FROM AUTHOR]

This paper proposes an electricity market model of Turkish electricity market for monthly and yearly electricity price forecasting in medium-term by means of supply and demand dynamics formed via a theoretical approach. The electricity market model created within this scope consists of three main components related to electricity demand, supply, and price segments along with hydro optimization submodel, which takes into account the nonlinear relation between supply and price. Electricity price is determined based on the intersection of demand curve and merit order curve that has dynamic behavior for dam-type hydrogeneration, import coal, and natural gas power plants. The paper aims to determine the range of possible electricity prices rather than a single price forecast by creating multiple scenarios based on the uncertainties in main variables affecting the electricity prices. Meanwhile, electricity generation portfolio with respect to market participants and primary energy resources as well as price forecasts can be obtained simultaneously. Ultimately, the model can identify how effective a variable of the market on the electricity price is. The developed method is validated via real data. [ABSTRACT FROM AUTHOR]

This paper examines the determinants of electricity generator's trading strategies in the U.S. Acid Rain Market. Model estimates show that the SO2 allowances market is de facto regionalized due to the regionalization of the electricity market. The national dimension only appears when there are local imbalances in the electricity market that give strong incentives to search for a better deal outside of the generator's regional market. We also identify the importance of counterpart differentiation and the influence on the counterpart choice of the regulatory framework, market evolution and transaction size. These findings are shown to be robust to Enron's abnormal behavior during 2000-2001 and its subsequent bankruptcy. The results suggest that, contrary to received knowledge, abatement costs are not the only consideration when trading pollution allowances: market microstructure can play a crucial role. [ABSTRACT FROM AUTHOR]

This paper investigates the current state of market integration among European electricity day-ahead spot prices. In our empirical analysis we utilize a large sample of hourly spot prices of 25 European markets for the period 2010Jan01/01h- 2015Jun30/24h and combine it with other relevant data such as hourly interconnector capacities and the existence of market coupling. Firstly, empirical results from cointegration analysis indicate that market integration increased from 2010 to 2012 but then declined until 2015, despite the introduction of market coupling in many markets. Secondly, we empirically assess error correction after price shocks and reach the conclusion that markets' strength of the error correction mechanism is rather modest. In general, our findings suggest that the integration among European electricity markets has a large potential for improvements from additional capacity investments and further promotion of market coupling. [ABSTRACT FROM AUTHOR]

The fourth Huaian pumping station is one of the second-stage pumping stations on the east route of the south-to-north water diversion project in China. The operation optimization problem of three pumps in the station is formulated to minimize the electricity cost while satisfying the flow demand. After analyzing the characteristics of the problem, a decomposition method is proposed to reduce the dimensionality of the optimization problem and thus the computation time. Simulation shows that the energy cost is reduced by 2.54 % compared with the benchmark scheduling based on the proposed method. In comparison with the decomposition/aggregation-dynamic programming method and the dynamic programming with successive approximation method, the proposed algorithm can effectively save electricity costs for Huaian pumping station. The case study shows that the cost efficiency comes from two aspects: demand shift from the time intervals with a high electricity price to those with a low electricity price, and the operation mode with high energy efficiency. The former is subject to the pumps’ capacity and the daily demand. The larger the pumps’ capacity is, the more demand can be shifted and thus the lower the cost is. The latter is subject to the pumps’ characteristics and the pump heads. The larger the head is, the smaller the difference for energy efficiencies with different blade angles are, and the smaller the energy savings with the optimal operation are. When the demand is high for a given pump head, demand shifting is the main reason, while the second aspect is the main reason when the demand is low. [ABSTRACT FROM AUTHOR]

Many irreversible long-run capital investments entail opportunities for managers to respond flexibly to changes in the economic environment. However, common levelized cost measures used to guide decision-making, such as the levelized cost of electricity, implicitly assume that the values of random economic variables are known with certainty when investment decisions are made. This assumption implies, often incorrectly, that managerial flexibility carries zero value. This paper improves levelized cost measures by deriving an expansion that accounts for both uncertainties in relevant variables and the value of managerial flexibility in responding to them. This method is applied to quantify the value of flexibility in two example decision problems. In one, an operator of a natural gas electricity generation facility evaluates whether to invest in carbon capture capabilities. Another considers retirement decisions for U.S. nuclear plants. These examples illustrate that simplified cost metrics can inaccurately guide decision-making by inflating cost estimates relative to the proposed levelized cost measure that accounts for uncertainty and flexibility. [ABSTRACT FROM AUTHOR]

Electricity markets can be designed in different ways. One rule that is sometimes enforced is the uniform purchase price. Under this pricing method, all the consumers pay the same price regardless of the zone they belong to. By contrast, each producer receives its zonal price. This asymmetry in the price paid and received makes the clearing process not easily treatable through standard optimization techniques. Within the framework of marginal pricing, this paper shows how it is possible to formulate the market clearing problem with uniform purchase price and zonal selling prices as a computationally tractable mixed integer linear programming problem. The proposed approach is tested using real data from the Italian day-ahead market, which is actually based on the aforementioned rule. [ABSTRACT FROM PUBLISHER]

Barriers to cross-border transmission on the United States--Mexico border, including labyrinthine permitting processes, have long impeded the development of valuable border-region power infrastructure. The historical origins of the electric power regulatory system offer some guidance for why the presidential permitting system exists in its present, tangled form. Recently, legislators have renewed efforts to amend cross-border infrastructure approval. Though these attempts have failed, a new set of legislative proposals has been making its way through the chambers and has a high chance of achieving success. This Note discusses these topics and closes by advocating for Congress to ease crossborder infrastructure permitting processes to benefit the economies and peoples on both sides of the United States-Mexico border. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to analyze and propose the United Arab Emirates (UAE) plan of Renewable Energy mix in 2030 to achieve the government target of reducing the greenhouse gas emissions of CO 2 while maintaining the electricity price to a minimum. The study includes many proposals of the energy mix for electricity to achieve the target, and a comparison study to IRENA's proposal to select the most suitable energy mix plan in terms of CO 2 reduction while maintaining the electricity price. The analysis has been conducted in two different cases: the first case analyzed where the government of UAE does not implement CO 2 taxation of hydrocarbon fuels used to generate electricity; however, the second case analyzed if the government apply CO 2 taxations what would the results be. Further on, calculations of the surplus energy were implemented for future studies on utilization of the excess energy or reducing the power capacity. This paper provides a background analysis of UAE's electricity consumptions and future government plans. [ABSTRACT FROM AUTHOR]

Implementing real-time electricity pricing in residential sector is supposed to be an effective measure to balance daily electricity load and promote electricity conservation. However, due to lack of empirical evaluation, evidence about the in-field impacts of real-time electricity pricing is still lacking in China. Based on household survey and agent-based modeling, this study simulates Chinese household's 24-h electricity demand profile, and analyzes how households adjust their electricity use activities under the dynamic real-time pricing scheme. Results show that real-time pricing has the potential to shift residential electricity load from peak to off-peak periods and reduce total electricity consumption. The magnitudes of these two effects hinge on the probability of households' response to electricity price increases. Survey results indicate that such probability varies along the two dimensions of households' characteristics and the type of electricity use activities. To raise the response rates and improve real-time pricing's effects, the following strategies are proposed: 1) introduce and subsidize the purchase of economically feasible domestic electricity storage devices; 2) integrate environmental education modules in the real-time pricing policy design, so as to raise household dwellers' sensitive to the price signals. [ABSTRACT FROM AUTHOR]

Industries are a major fossil energy consumer and economic development contributor in China. The Chinese government is performing the industrial reforms to diminishing the dependence of industrial output on fossil energy consumption. To guide the present industrial policy adjustment, this study employs the Tapio decoupling index, a structural decomposition algorithm, a hybrid forecasting model, and industry-related data from 2001 to 2014 to evaluate, decompose and forecast the relationship between China's industrial output and fossil energy consumption. Empirical results show that the decoupling index of fossil energy consumption from the value added by China's industry was less than 0 in 2015. Consequently, the industrial fossil energy consumption reached its peak at that time and will gradually decrease in the future, even though the added value continuously increased. The mitigation goals set by the Chinese government for industrial fossil energy intensity by 2020 are expected to be achieved ahead of schedule. The attainment of these goals will strongly support the realization of China's fossil energy-related prospects for 2030. To reduce industrial fossil energy consumption, China should develop strategies for non-fossil energy electricity generation, implement electricity price bidding, prevent overheated investments to real estate and infrastructure, and eliminate backward capacity and establish market-access rules for acetic acid, calcium carbide, ethyl alcohol, and dimethyl ether. [ABSTRACT FROM AUTHOR]

Summary: Integrating variable renewable energy from wind farms into power grids presents challenges for system operation, control, and stability due to the intermittent nature of wind power. One of the most promising solutions is the use of compressed air energy storage (CAES). The main purpose of this paper is to examine the technical and economic potential for use of CAES systems in the grid integration. To carry out this study, 2 CAES plant configurations: adiabatic CAES (A‐CAES) and diabatic CAES (D‐CAES) were modelled and simulated by using the process simulation software ECLIPSE. The nominal compression and power generation of both systems were given at 100 and 140 MWe, respectively. Technical results showed that the overall energy efficiency of the A‐CAES was 65.6%, considerably better than that of the D‐CAES at 54.2%. However, it could be seen in the economic analysis that the breakeven electricity selling price (BESP) of the A‐CAES system was much higher than that of the D‐CAES system at €144/MWh and €91/MWh, respectively. In order to compete with large‐scale fossil fuel power plants, we found that a CO2 taxation scheme (with an assumed CO2‐tax of €20/tonne) improved the economic performance of both CAES systems significantly. This advantage is maximised if the CAES systems use low carbon electricity during its compression cycle, either through access to special tariffs at times of low carbon intensity on the grid, or by direct coupling to a clean energy source, for example a 100‐MW class wind farm. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) Panels, one of the more promising renewable energy technologies, are growing rapidly nowadays, especially in developed countries. However, these systems have not achieved public acceptance in some countries due to low energy efficiency and poor economic performance, especially in countries which are subsidized in energy tariffs. In this paper, the energy and economic performance of fourteen rooftop PV systems with the power of 5 kW in the hot and dry climate of Iran are assessed by monitoring the total annual energy production and simulation. The monitored data is used to analyze systems’ economic performance via Pay-Back Period (PBP), Net Present Value (NPV), Return of Investment (ROI) and Levelized Cost of Energy (LCOE). Results show that single array configuration systems have the maximum energy production while dividing the system decreases the production. Economic analysis shows that the average PBP is 11.6 years under actual price of electricity (0.21$), however it is 46.9–50.5 years under subsidized average tariffs. ROI values range from 2.6 to 3.2 with the average of 2.9 for actual prices. Under subsidized prices, the cash generated by investment cannot even offset the costs that the investment requires during its lifetime with NCF and NPV being both negative. Overall, the systems are not economically beneficial under subsidized average tariffs in Iran, which discourages private and public sectors to investment on these systems. Environmentally, each PV system can averagely reduce 500 kg CO 2 emission in the first year of installation and fourteen of them can approximately reduce 1,613,900 kg of CO 2 emission during life time of PV panels. [ABSTRACT FROM AUTHOR]

Currently, the integration of European Electricity Market (EEM) has led to a single European Day-Ahead Market (DAM) with multiple-areas considered as bidding zones. In the near future, the EEM will spread to the Intra-day and Balancing market. To operate the DAM, a market clearing tool (algorithm) has been developed by market operators. The development of this algorithm corresponds to three primary principles: (i) one single framework, (ii) robust operation, and (iii) individual accountability. However, this algorithm is not available to the research community. In this paper, the authors develop a complete European DAM model in General Algebraic Modelling System (GAMS), formulating it as a Mix Integer Quadratic Constraint Problem (MIQCP) and iterative procedure, to mitigate the non-convexity of electricity prices across Europe due to the “fill or kill” condition of block, complex and Prezzo Unico Nazionale (PUN) orders. Eventually, two case studies reflecting the current European DAM evaluated the model, aiming to confirm its robustness and reliability. [ABSTRACT FROM AUTHOR]

In developing countries, mini-grids are seen as an important option to improve electrification rates in rural areas. In order to be successful, mini-grids face issues of operation and sizing of generation capacities. Current studies on the optimal sizing of mini-grids do not include capacity expansion feedbacks regarding the operator's or investor's long-term economic performance on growth in electricity usage, e.g. gap between demand and supply impacting the operator's income. Using a System Dynamics model, this paper compares the impact from two capacity expansion strategies on rural mini-grid operator's long-term economic performance. The two capacity expansion strategies are: a strategy with minimized costs and a strategy where only diesel power is allowed. Research shows that a cost-minimized capacity expansion strategy might not be the most beneficial solution for the operator's long term financial performance. Specifically, the high investment costs prohibit the implementation of the cost-minimized expansion strategy. In addition, the diesel-only expansion strategy suffers from high operational costs, which creates long-term challenges as the share of diesel increase. Therefore, the timeline of the investments and when to implement different strategies is important, creating a benefit for a System Dynamics approach. [ABSTRACT FROM AUTHOR]

As a secondary energy, electricity is an important channel between original energy and energy consumers. Electricity price is a critical factor for the interests of all involvers in the electric power market. It also plays an important role for the sustainable development of energy and environment. Smart grid is a new conception proposed in recent years to improve the intelligent level and increase the efficiency of electric power system operation. Smart grid combines and integrates information technology, communication technology and intelligent control technology with tradition power system. To achieve the many objectives of smart grid, Demand response (DR), as an effective technique of demand side management (DSM), refers to the changes in electricity consumption behavior of users in response to the dynamic price or incentive rewards. Price based demand response (PBDR) is one of the two major DR programs. In this paper, we first introduce the pricing theories in economics, the pricing of electricity and the development of electricity pricing in China. Then, we present a detailed discussion on the PBDR strategies in the DSM of smart grid. Also, the research status of PBDR is reviewed. Finally, it gives a summary of the whole paper in the last Section. [ABSTRACT FROM AUTHOR]

This paper examines the technoeconomic optimization of the size of valve-regulated lead acid-type battery energy storage systems (BESSs) in order to minimize the total annual operating cost of a grid-connected photovoltaic BESS within the framework of system operational constraints, using the improved harmony search algorithm. The electricity cost is calculated using time of use pricing, where the price of energy varies according to the load demand. The concept of feed-in-tariff is discussed, where power can be bought from and sold back to the grid. A sensitivity analysis is performed for three cases: without storage, with storage and without peak load shaving, and with both storage and peak load shaving. The total annual operating cost of the system is calculated for all three cases and cost savings are compared. The simulations, performed with MATLAB, show a good optimization performance. [ABSTRACT FROM AUTHOR]

In this paper, the thermo-economic optimization of the waste heat based organic Rankine cycle powered cascaded vapor compression-absorption refrigeration system is presented. Organic Rankine cycle with dry organic working fluid is used as a power generating cycle to provide input to the vapor compression refrigeration system. Moreover, the high temperature organic working fluid at the expander outlet is used to supply thermal need of the vapor absorption refrigeration system. The present system achieves low temperature cooling efficiently. However, initial capital cost and complexity are the practical limitations for the present system. The energetic efficiency of the present system for only cooling mode and cogeneration mode (cooling and heating) are calculated to be 22.3% and 79%, respectively. It may be noted that the extra heat available, apart from the thermal energy requirement of the vapor absorption system, is taken as process heat in the cogeneration mode. The simple payback period and break-even point are calculated (for the base case) to be 5.26 years and 4.22 years, respectively. The system size and annualized cost are optimized, using nonlinear programming based on conjugated directions method, to make the system potentially attractive for the industrial sector. Optimization results reveal that the annualized cost of the present system is decreased by about 12% compared to the base case. Moreover, the simple payback period and break-even point are reduced to 4.50 years and 3.48 years, respectively. The results of comparative economic study, between the present and stand-alone vapor compression refrigeration systems, show that the higher value of electricity price and the lower value of discount rate are favorable for the selection of the present system. [ABSTRACT FROM AUTHOR]

The Organic Rankine (ORC) and Kalina (KC) Cycles represent two different technologies suitable to exploit low and medium temperature geothermal heat resources. In this work, the performances of KC, CO 2 and ORC cycles, the latter using different working fluids, for power generation from two geothermal fluid reservoirs are compared from an energy and exergoeconomic perspective. Two different case studies are discussed: the first one referred to a medium-temperature heat source of 212 °C (Mt. Amiata, Italy), the second one to a low-temperature heat source of 120 °C (Pomarance geothermal basin, Italy). For each case study, cost rate balances and auxiliary equations for all components were evaluated, as well as the flow rate and unit exergy cost for each stream. The results for the medium temperature case study showed that, among the considered cycles, an ORC with R1233zd(E) achieves the best exergoeconomic performance. The cost of the produced electricity was found to be 8.85 c€/kW h, which is 3% lower than that of the KC. On the other hand, for the low temperature case study, the KC shows the best performance, being able to produce 22–42% more net power than the ORC; in this case, the cost of electricity produced by the KC was found at 12.5 c€/kW h, which is 24–34% lower than the typical value for an ORC with different working fluids. [ABSTRACT FROM AUTHOR]

Several parts of the Mediterranean basin are experiencing grid parity nowadays, which is a sign that Photovoltaic (PV) technology has become competitive to conventional energy sources and may continue to develop without support. The net-metering policy takes advantage of the grid parity situation and is considered a step away from fading Feed-in Tariff schemes. Nevertheless, several policy variations make the decision about a specific net-metering scheme a complicated task. Therefore, the aim of this paper that deals with residential PV systems is two-fold. The first aim is to present a methodology that identifies the appropriate general net-metering scheme given the particularities and local conditions. The second goal is to examine the parity situation and compose net-metering policy recommendations in 6 Mediterranean countries. The current net-metering policy in Cyprus, which sets a grid charge on PV prosumers, is used as a case study under 3 scenarios. The first simulates the current policy in Cyprus, the second simulates self-consumption and different partial-netting schemes, and the third a partial-netting scheme with variable netted network charges. The results reveal that partial-netting policies with shorter timeframes of rolling credits and higher grid charges offer advantages that can be utilized for effective net-metering policies. [ABSTRACT FROM AUTHOR]

The price effect of the rising share of renewable electricity, which is called ‘merit-order-effect’, leads to noticeable changes in the German power industry and debates about the electricity market design. This paper estimates the merit-order-effect induced by variable renewable energy in the German-Austrian electricity sector with a multivariate regression model. The research focus lies on the impact of the estimated effects on the marketability of variable renewable electricity generation. The results show a systematic decline of the average market revenues for wind and photovoltaic plants in the period from January 2011 to December 2013. Current market data shows a continuation of this trend into 2016. According to the German long term goals for the use of renewables, wind and solar power will play a crucial role in the future electricity generation mix. If investments in these technologies will be profitable without any regulatory remuneration mechanisms in addition to the market revenues, depends on the further cost degression and the development of the merit-order-effect. [ABSTRACT FROM AUTHOR]

The excessive combustion of fossil fuels for energy provision have altered natural planetary functions, resulting in adverse biophysical and societal implications. Such implications have prompted many governments globally to advocate for the adoption of renewable energy systems in order to reduce GHG emissions. While renewable energy technologies such as solar and biogases have been thoroughly researched and deployed, tidal current turbines (TCTs) that harness kinetic energy from the lateral movement of the tides are a comparatively emerging renewable energy technology, and thus has received relatively less attention with respect to their potential to supplement the renewable energy transition. This paper examines the physics behind tidal movements and cycles, and the technological operation of TCTs. Environmental impacts and economic barriers are analyzed. Best practices of MSP from world leading nations are examined, along with current deploy-andmonitor-consenting regimes of TCT test facilities. An optimal TCT design is suggested based on a synthesis of information from proceeding sections. Finally, an analysis of the implementation of TCTs in Canada, China, and Norway is presented, the results of which demonstrate that harnessing the accessible and sustainably extractable resource of each nation can result in an aggregate installed capacity of 9076 MW through the deployment of 7564 TCTs at a cost of $5,740,964,430, thereby creating 14,467 jobs. This would produce 29,829,711 MW h/yr of electricity sold at approximately 22 cents/kWh, eliminating a total of 14,914,855,258 kg of CO2e, approximately 0.1%. of the projected global electricity demand for 2016. [ABSTRACT FROM AUTHOR]

SUMMARY There is a danger of output suppression of high-penetration residential photovoltaic systems due to voltage increase. It is necessa1ry to install new technology to prevent the occurrence of such phenomenon. Therefore, we focused our attention on heat-pump water heaters (HPWHs). HPWHs are usually used to heat water during nighttime because electricity prices are cheaper than during the daytime for the load leveling in Japan. So they can be used as a countermeasure without additional cost if they are operated during the daytime. However, HPWHs do not have sufficient capacity to absorb inverse energy at each residence. Thus, HPWH operation must be optimized to minimize output suppression loss. In this research, we selected four typical sunny days in spring, summer, autumn, and winter. The optimal HPWH operation was calculated by numerical simulation. The optimal monthly HPWH operation was investigated using the weather forecast assuming actual operation in each season. [ABSTRACT FROM AUTHOR]

Electricity storage (ES) has the potential of offering several energy system benefits but different technologies also offer different services which can be traded on different markets. In this study, a combined assessment methodology is proposed, enabling a benchmark comparison of stationary electricity storage technologies for different time and system scales, considering their technical, economic and environmental performance. The results show that for short time scale (0.01 h), battery stands out with an advantage in terms of levelised costs, while Advanced Adiabatic (AA-) and Isothermal (I-) Compressed Air Energy Storage (CAES) have relatively low life cycle Greenhouse Gas (GHG) emissions. For the medium time scale (4.5 h), I-CAES shows the best performance for small scale systems, while for large scale systems, Pumped Hydro Storage (PHS) and AA-CAES show best performance. In our long time scale (seasonal) scenario, Power-to-gas-to-power (P2G2P) has lower levelised costs due to low or avoided investment for storage of gas, but higher GHG emissions than other technologies. If existing reservoirs can be utilized for PHS, it can be economically competitive to P2G2P for seasonal storage. However, storage capacity required for seasonal storage should also be taken into account, for which P2G2P has more flexibility. [ABSTRACT FROM AUTHOR]

A cradle-to-gate life cycle assessment of rice straw-based synthetic natural gas (R-SNG) production was carried out in this study, and the inventories of rice straw cultivating and harvesting, feedstock transportation, R-SNG facility construction and dismantling, and power consumption (i.e. purchased electricity (PE) scheme and self-supply power (SP) scheme) stages were analyzed. Then, the inventory data was characterized and normalized. The results showed that the SP scheme can significantly reduce the environmental impacts of R-SNG product. A comparison of R-SNG, natural gas (NG) and coal-based synthetic natural gas (C-SNG) was conducted from the perspective of environmental impacts. The results showed that the amount of CO 2 emission from C-SNG was the largest, followed by that from NG, while the amount of CO 2 emission from R-SNG was negative. A sensitivity analysis about allocation principles of the rice straw and rough rice was carried out to evaluate the allocation principles on the environmental impacts. In conclusion, R-SNG showed great environmental benefits in the abiotic resources saving and greenhouse gases emission reduction. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic control algorithm to enable an energy-aware single machine scheduling under the time-varying electricity pricing policy, in which price rates remain fixed day-to-day over the season. The key issue is to assign a set of jobs to available time periods where different electricity prices are assigned, while considering requested due dates of jobs so as to minimize total penalty costs for earliness and tardiness of jobs and total energy consumption costs, simultaneously. As the first contribution of this study, we develop a new mixed integer nonlinear programming (MINLP) model that aims at determining job arrival times and resulting earliness and tardiness of jobs and energy consumption costs for machine idle and normal processing. Second, an efficient heuristic approach based on continuous-time variable control models and algorithm is developed. The proposed heuristic adaptively changes job arrival times and due dates, which finally determine production sequence over the time periods of different electricity prices, machine turn-off, and machine idle with minimum energy consumption costs and just-in-time (JIT) penalty. Energy and JIT performance of the proposed approach is examined using real energy and machining parameters of a HAAS machine and compared to those of the metaheuristic approach. For relatively large size data groups, the proposed approach incurs about 4 ∼ 11 % higher energy consumption costs on average, which are offset by up to 99 % lower JIT costs, resulting in 10 ∼ 94 % lower total costs on average compared to the metaheuristic approach. The proposed time-scaled heuristic algorithm yields extremely short computational time, which enables production managers to flexibly select proper production strategies and to implement them for different production environments. [ABSTRACT FROM AUTHOR]

Recent literature has shown that electricity demand elasticities may not be constant over time and this has investigated using time-varying estimation methods. As accurate modeling of electricity demand is very important in Azerbaijan, which is a transitional country facing significant change in its economic outlook, we analyze whether the response of electricity demand to income and price is varying over time in this economy. We employed the Time-Varying Coefficient cointegration approach, a cutting-edge time-varying estimation method. We find evidence that income elasticity demonstrates sizeable variation for the period of investigation ranging from 0.48% to 0.56%. The study has some useful policy implications related to the income and price aspects of the electricity consumption in Azerbaijan. [ABSTRACT FROM AUTHOR]

Existing studies that consider the techno-economics of residential heating systems typically focus on their performance within present-day energy systems. However, the energy system within which these technologies operate will need to change radically if climate change mitigation is to be achieved. This article addresses this problem by modelling small-scale heating techno-economics in the context of significant electricity system decarbonisation. The current electricity market price regime based on short run marginal costs is seen to provide a very weak investment signal for electricity system investors, so an electricity price regime based on long run marginal energy costs is also considered, using a case study of the UK in 2035. The economic case for conventional boilers remains stronger in most dwelling types. The exception to this is for dwellings with high annual heat demand. Sensitivity studies demonstrate the impact of factors such as price of natural gas, carbon intensity of the central grid and thermodynamic performance. Fuel cell micro combined heat and power shows most potential under the long run electricity price regime, and heat pumps under the short run electricity price regime. This difference highlights the importance of future electricity market structure on consumer choice of heating systems in the future. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective problem, whose resolution is carried out using a task management methodology and an evolutionary algorithm. The multi-objective problem includes demand response to real-time prices (RTP). Two objectives have been considered: the daily cost of electricity and consumer dissatisfaction, minimizing both. Hourly prices corresponding to a tariff currently existing in Spain have been used to evaluate the daily cost of the consumed electricity. The degree of compliance with the daily task programming required by the user has been used to evaluate the dissatisfaction of the consumer. Using a multi-objective evolutionary algorithm (NSGA-II), it has been possible to use the concept of Pareto optimality in order to determine the best solutions for the problem. The results show that the applied methodology can lead to cost savings between 6% and 12% without substantially changing the consumption habits of the consumer. If the consumer is willing to change his/her consumption habits, then the cost savings can reach 50%. In addition to cost savings, this methodology can reduce energy losses in the electric grid because the shift of consumption from peak to off-peak hours. [ABSTRACT FROM AUTHOR]

Trading of wind energy in day-ahead electricity markets entails significant risks. Wind traders are, therefore, interested in mitigating their trading risks in a number of ways. This work suggests addressing this issue in a comprehensive manner by coordinating the trading of wind resources with thermal resources and a pumped-hydro storage system (PHS) and co-optimizing the trading in energy and regulation markets. The trading strategy is modeled as a mixed integer linear stochastic program that takes into account several uncertain parameters, including wind power outputs, prices for energy, balancing, and regulation, and regulation deployments. In this optimization, the trader’s attitude toward risks is controlled by using the metric of conditional value at risk (CVaR). Simulation results show that coordinated and co-optimized trading of the generation resources and PHS significantly improve the total expected profits and mitigates trader’s risks. [ABSTRACT FROM AUTHOR]

The influence of different control strategies and boundary conditions on heat pump system performance are investigated in this study and the trade-off between complexity and performance of different controllers is addressed. For this purpose five different control approaches for a variable speed air source heat pump in a multi family house are compared for three different use-cases. The used controls differ in complexity and the use of external input data like price and weather forecasts. The use-cases are: Constant electricity prices, time variable electricity prices and PV self-consumption. Four different rule-based controllers are compared to a convex MPC approach, presented in this work. Results show that the MPC approach reduces annual operating cost by 6–11% for constant electricity prices and 6–16% in the case of variable electricity prices. Rule-based approaches lead to cost reductions of 2–4%. MPC could increases PV self-consumption from 56% to 58% up to 64–71%. The rule base approaches are found computationally less demanding and easier to design. However fine-tuning has been considerable work and with changing boundary conditions rules had to be readjusted. It showed that increasing thermal storage without MPC is not beneficial and optimised controls are a prerequisite to benefit from increased storage sizes. [ABSTRACT FROM AUTHOR]

The work presented in this paper is focused on the single-tube configuration for linear Fresnel receiver. Specifically, this article analyses the feasibility of a new configuration: a hybrid loop in which the first modules, at lower working temperature, use non-evacuated receivers with medium thermal performance, and the remaining, at greater temperature, use evacuated receivers with high thermal performance. The non-evacuated receiver considered in this work consists of a pyromark tube inside a cavity provided with a secondary reflector opened to the ambient. The second one is based on an evacuated tube, similar to those of parabolic trough, and it is also inside an opened cavity with a secondary reflector. In the first place, this work performs a comparative analysis of the heat loss associated to the three single-tube receiver designs proposed: evacuated, non-evacuated and non-evacuated with a glass plate at the aperture. The heat loss is quantified in each case as a function of three parameters: the temperature difference between the tube wall and the environment, the incident concentrated flux on the tube and the wind velocity. Although the evacuated design has the lowest heat loss, the differences with the other two designs decrease as the working temperature is lower. Thus, a hybrid loop is proposed, which consists of non-evacuated modules at the beginning and evacuated modules at the end. The lower investment of the non-evacuated tubes, as well as their robustness and simplicity, can compensate for the lower energy absorbed by these type of receivers. Next, a study of the annual electricity cost produced by Fresnel plants with hybrid loop is performed. The work concludes that there are specific configurations of hybrid loops that minimize the electricity cost, when the different investment, operation and maintenance costs of the receivers are quantified. The maximum percentage of non-evacuated tubes in the loop will depend on the technology and the working temperature range. [ABSTRACT FROM AUTHOR]

There is a growing worldwide concern regarding emissions of energy-related greenhouse gases (GHGs). Policy, technology and human awareness provide opportunities for offsetting these effects through energy conservation, efficient appliances and solar generation in households as these have been the subject of increasing attention across the world in recent years. Solar rooftop generation, in particular, could deliver significant efficiency improvements and reductions of CO2 emissions through its diffusion, which also avoids losses in electricity transmission and distribution systems; energy efficiency contributes to the achievement of a sustainable future because it reduces the energy-infrastructure needs, increases competitiveness and improves consumer welfare; conservation would also reduce CO2 emissions in households. The purpose of this paper is to assess the combined effect of the diffusion of solar rooftop generation, energy efficiency and electricity conservation in the residential sector in Colombia. Simulation results for different policy scenarios indicate that rooftop generation, energy efficiency and conservation can together lead, under certain conditions, to important reductions in both electricity prices and emissions. [ABSTRACT FROM AUTHOR]

Urban form is an important driver of energy demand and therefore of GHG emissions in urban areas. Yet, research on urban form and energy remains sectorial and hasn’t been able to deliver a full understanding of the impact of the physical structure of cities upon their energy demand. Most common approaches feature engineering models in buildings, and statistical models in transports. This study aims at contributing to the characterization of the link between urban form and energy considering altogether three distinct energy uses: ambient heating and cooling in buildings, and travel. A high-resolution methodology is proposed. It applies GIS to provide the analysis with a spatially-explicit character, and neural networks to model energy demand based on a set of relevant urban form indicators. The results confirm that the effect of urban form indicators on the overall energy needs is far from being negligible. In particular, the number of floors, the diversity of activities within a walking reach, the floor area and the subdivision of blocks evidenced a significant impact on the overall energy demand of the case study analyzed. [ABSTRACT FROM AUTHOR]

The residential progressive electricity tariffs (RPETs) in South Korea were recently changed, which can significantly affect the economic viability of the solar photovoltaic system. In this regard, this study aimed to investigate the economic impact of RPETs in implementing the building-integrated photovoltaic blind (BIPB) using an advanced finite element model. First, the nine-node-based finite element model with improved prediction accuracy for estimating the technical performance of the BIPB (FEM 9-node BIPB) was developed. Second, the life cycle cost approach was used to evaluate the effect of RPETs on the economic viability of the BIPB in terms of the energy demand and supply. The main findings of this study can be summarized as follows. First, the prediction accuracy of the developed FEM 9-node BIPB was proved to be superior to the existing model, in which the estimation results can be found within a short time in an easy way. Second, considering the RPETs, it showed a tendency that the higher the amount of electricity consumption in a household was, the more improved the economic viability of the BIPB became. However, it should be also analyzed which tariff zones in the RPETs can be covered by the amount of electricity generated from the BIPB, because it can significantly affect the economic viability of the BIPB. Third, after the revision of the RPETs, the economic viability of the BIPB under the grid-connected utilization plan was determined to be superior. This study can be used for end-user to maximize the economic viability in implementing the BIPB. [ABSTRACT FROM AUTHOR]

This paper developed an agent-based model (ABM) to explore the energy saving potentials (ESPs) of various types of appliances in offices under different pricing mechanisms. The model included four types of commonly used appliances in office buildings: an air conditioner (AC), computers, lights and a basic load. The total ESPs of the entire office are 6.7% and 17.4% on the second and the third price tier of the tiered pricing mechanism (TEP), while the ESPs are 11.8% and 14.2% under the peak-valley pricing (PVP) and critical peak pricing (CPP), respectively. Within different types of appliances, AC consumes the largest amount of electricity, over 50%, while the ESPs of the AC under different pricing mechanisms are only 6.9–12.1%. In contrast, the lights have the biggest ESP, i.e. 14.1–53.4%, under various pricing levels. Both the pricing mechanisms of PVP and CPP only have the effect of peak clipping and do not have a significant effect of valley filling, since there is no people working in the office during the valley price period. The maximum ESP, which is based on people’s maximum-saving behavior, is much larger than the ESPs on the basis of people’s ordinary consumption patterns. This implies the importance of improving people’s awareness of energy saving and refining their behaviors. Lastly, the model developed in this study provides a generic platform for simulating many types of energy systems and is very effective for handling the complicated relations between different types of technology and the way how they are used and interacted with each other. ABMs have very good adaptability and capacity in simulating energy systems. [ABSTRACT FROM AUTHOR]

In certain electricity markets, because of nonconvexities that arise from their operating characteristics, generators that follow the independent system operator's (ISO's) decisions may fail to recover their cost through sales of energy at locational marginal prices. The ISO makes discriminatory side payments to incentivize the compliance of generators. Convex hull pricing is a uniform pricing scheme that minimizes these side payments. The Lagrangian dual problem of the unit commitment problem has been solved in the dual space to determine convex hull prices. However, this approach is computationally expensive. We propose a polynomially solvable primal formulation for the Lagrangian dual problem. This formulation explicitly describes for each generating unit the convex hull of its feasible set and the convex envelope of its cost function. We cast our formulation as a second-order cone program when the cost functions are quadratic, and a linear program when the cost functions are piecewise linear. A 96-period 76-unit transmission-constrained example is solved in less than 15 s on a personal computer. [ABSTRACT FROM PUBLISHER]

Demand response mechanisms and load control in the electricity market represent an important area of research at the international level: the trend towards competition and market liberalization has led to the development of methodologies and tools to support energy providers. Demand side management helps energy suppliers to reduce the peak demand and remodel load profiles. This work is intended to support energy suppliers and policy makers in developing strategies to act on the behavior of energy consumers, with the aim to make a more efficient use of energy. We develop a non-linear optimization model for the dynamics of the electricity market, which can be used to obtain tariff recommendations or for setting the goals of a sensibilization campaign. The model comes in two variants: a stochastic version, designed for residential electricity consumption, and a deterministic version, suitable for large electricity users (e.g., public buildings, industrial users). We have tested ourmodel on data from the Italian energy market and performed an extensive analysis of different scenarios. We also tested the optimization model in a real setting in the context of the FP7 DAREED project (http://www.dareed.eu/), where the model has been employed to provide tariff recommendations or to help the identification of goals for local policies. [ABSTRACT FROM AUTHOR]

The abandonment of wind and PV power has become the Achilles heel that restricts the development of renewable energy in China, which is associated with institutional defects in China's Renewable Energy Law. As a priority area in energy development, renewable energy is not substantialized. Instead, it is undermined because the guarantee system for purchasing electricity, generated by the use of renewable energy resources, in full amount, is alienated by lower-level laws. The unestablished mandatory legal obligations and responsibilities in power grid transmission networks lead to difficulties in renewable energy power generation, transmission, and accommodation. Due to the lack of continuity and stability in the feed-in tariff and subsidy policies for renewable energy, investors cannot have reasonable expectations. China's Renewable Energy Law must be remedied to correct these existing institutional defects, and to solve the problem of wind and PV power abandonment in order to promote the healthy development of renewable energy. [ABSTRACT FROM AUTHOR]

It is few to real-time pricing( RTP) according to subscribers’ classification in the present smart grid research. As the big difference of various electricity price in China,this paper classified the subscribers as the residents,commercial and industrial subscribers and built the utility functions based on the concept of microeconomics for the classified subscribers. The iteration based on a Lagrangian dual algorithm obtained the optimal power consumption,the real-time electricity price and the aggregate welfare of the classified subscribers. Compared with time of use( TOU) ladder electricity price in Shanghai,the electricity consumption of all subscribers is more stable and the welfare of industrial and commercial subscribers is higher after using the RTP. The electricity behavior of residential subscribers is the most sensitive to the RTP and has a big fluctuation and the welfare of residential subscribers using RTP is lower than that using TOU ladder electricity price. The proposed RTP model and algorithms have the advantages of peak load shifting,smoothing the aggregate load,balancing the supply and demand of the power grid,and improving the total welfare benefits of all subscribers. [ABSTRACT FROM AUTHOR]

Estimating the embodied emissions in electricity has been a challenge due to the interconnectedness of electrical grids. Previous studies use a variety of methods that are often inaccurate or difficult to implement, lacking a standardized tool. We propose a method adapted from the economic input-output theory, which we term as “Quasi-Input-Output (QIO)” model, to evaluate embodied emissions in purchased electricity. The method takes into account the difference between the natures of trade in electricity and in goods and services, able to capture the effects of both direct and higher-order electricity transfers in the network. We use the Eurasian Continent grid network as a case, identifying regions where inter-grid electricity transfers, both direct and high-order, have sizable impacts on estimated emission factors of purchased electricity. Overall, while ignoring electricity trade can result in errors in embodied emissions estimation, directly adjusting for electricity trade (neglecting higher-order trade) tends to generate inaccuracies in the opposite direction. Our model can be potentially applied as a standard tool for the accounting of embodied emissions in purchased electricity in inter-grid electricity trade systems. It also provides a foundation for further applications of input-output theory in the analysis of demand-side drivers for environmental impacts of interconnected grids. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel game model based on the hierarchical Stackelberg game for analyzing the multiple energies trading (MET) problem in integrated energy systems (IESs). In the proposed game model, a number of distributed energy stations (DESs) lead the game deciding the unit prices of electricity and cooling energies they generated, while multiple energy users (EUs) perform as followers determining the amounts of energies to consume. In addition to maximizing the profit of each DES, the proposed hierarchical game model also considers the benefit of each EU, who actively participates in the MET. We prove that, for the first time, there exists a unique Stackelberg Equilibrium (SE) in the MET, so that the existence of an equilibrium strategy optimizing the objectives of all participants can be guaranteed. Moreover, we found that the price setting game played by DESs is a submodular game when energy dispatching is performed during their generation process, which indicates that the behaviors of DESs are strategic substitutes. Furthermore, the SE is obtained in a closed-form, by which the effects of coupling generation of multiple energies are analyzed. Finally, a best response algorithm is provided to obtain the SE in an iterative way. Numerical studies demonstrate the convergency of the proposed best response algorithm, corroborate the jointly effects of market scale and exogenous parameter on the SE and verify the practicability of the proposed game-theoretic approach. [ABSTRACT FROM AUTHOR]

While the management of PV generation is the prime application of residential batteries, they can deliver additional services in order to help systems to become cost-competitive. They can level-out the demand and potentially reduce the cost and emissions of the energy system by reducing demand peaks. In this study, community energy storage (CES) is optimised to perform both PV energy time-shift and demand load shifting (using retail tariffs with varying prices blocks) simultaneously. The optimisation method obtains the techno-economic benefits of CES systems as a function of the size of the community ranging from a single home to a 100-home community in two different scenarios for the United Kingdom: the year 2020 and a hypothetical zero emissions target. It is demonstrated that the levelised cost and levelised value of CES systems reach intermediate values to those achieved when both applications are performed independently. For the optimal performance of a battery system being charged from both local PV plants and the grid, our results suggest that the battery should be sized suitable to ensure it can fully discharge during the peak period. [ABSTRACT FROM AUTHOR]

The rapid growth of mobile communication technology and the corresponding significant increase in the number of cellular base stations (BSs) have increased operational expenses (OPEX) for mobile operators, due to increased electricity prices and fossil fuel consumption. Thus, identifying alternative solutions to reduce OPEX has become a major priority of mobile operators. Solar energy is considered an economically attractive and eco-friendly option. This paper examines solar energy solutions for different generations of mobile communications by conducting a comparative analysis of solar-powered BSs based on three aspects: architecture, energy production, and optimal system cost. In addition, the economic feasibility of the solar energy solution compared with conventional sources is discussed. The simulation results suggest that solar-powered BSs would be a suitable long-term solution for the mobile cellular network industry. [ABSTRACT FROM AUTHOR]